{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code\appendix_log.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}16 Nov 2018, 15:26:47
{txt}
{com}. 
. clear
{txt}
{com}. 
. set more off
{txt}
{com}. 
. use "datasetNZballot_Aug2017_prepared.dta", clear
{txt}(Written by R.              )

{com}. 
. 
. ***************************************************************************
. **Descriptive Statistics***************************************************
. ***************************************************************************
. *Appendix (Table A1)
. 
. outreg2 using descriptive_stats, tex replace sum(log) label keep(benefits_ord benefits benefits_correct extracted bill_passed list_constnum  career_minister_pre committee_chair)

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}ballotyear {c |}{res}      3,056    2013.539    2.184403       2009       2016
{txt}{space 2}gifts_lead {c |}{res}      2,267    .1790913    .3835134          0          1
{txt}payments_l~d {c |}{res}      2,267    .0599912    .2375229          0          1
{txt}{space 8}bill {c |}{res}      3,056    178.9764    105.9673          1        362
{txt}{space 3}extracted {c |}{res}      3,056    .0382853    .1919157          0          1
{txt}{hline 13}{c +}{hline 57}
{space 3}ballotday {c |}{res}      3,056     15.0481    7.793991          1         30
{txt}{space 1}ballotmonth {c |}{res}      3,056    6.274869    3.365421          1         11
{txt}{space 1}prel_ballot {c |}{res}        104    .0576923    .3061481          0          2
{txt}{space 5}MP_bill {c |}{res}        117    39.74359    22.70544          1         76
{txt}{space 5}bill_id {c |}{res}        117     50.8547    29.54461          1        102
{txt}{hline 13}{c +}{hline 57}
introducti~e {c |}{res}        116    19661.23    834.3923      18164      20796
{txt}{space 3}committee {c |}{res}         31    5.967742    2.903835          1         10
{txt}first_read~g {c |}{res}        117    .6153846    .4885968          0          1
{txt}second_rea~g {c |}{res}        111     .045045    .2083436          0          1
{txt}third_read~g {c |}{res}        111     .018018    .1336197          0          1
{txt}{hline 13}{c +}{hline 57}
{space 1}bill_passed {c |}{res}        117    .1367521    .3450633          0          1
{txt}{space 5}ongoing {c |}{res}        117    .1880342    .3924201          0          1
{txt}adoption_d~e {c |}{res}         16    19846.81     644.293      18611      20793
{txt}rejection_~e {c |}{res}         79    19842.65    809.2449      18240      20921
{txt}interests_~e {c |}{res}      2,930    2014.549    2.189029       2010       2017
{txt}{hline 13}{c +}{hline 57}
{space 7}gifts {c |}{res}      3,056    .2153141    .4111074          0          1
{txt}{space 4}payments {c |}{res}      3,056    .0804974    .2721062          0          1
{txt}election_e~t {c |}{res}        456    2013.059    1.393392       2011       2014
{txt}outofparli~t {c |}{res}      3,056    .1492147    .3563582          0          1
{txt}expensesyear {c |}{res}      3,056    2013.539    2.184403       2009       2016
{txt}{hline 13}{c +}{hline 57}
{space 1}expensesTOT {c |}{res}      3,056    69943.29    23337.46        406     202759
{txt}{space 2}votechange {c |}{res}        609   -.0209524    6.221847     -25.12      12.31
{txt}{space 2}listchange {c |}{res}      1,086   -.0754939    .1489143  -.4877451   .3188854
{txt}list_const~m {c |}{res}      3,056    .4990183    .5000809          0          1
{txt}{space 3}bill_kill {c |}{res}        117    1.051282    1.394856          0          3
{txt}{hline 13}{c +}{hline 57}
{space 3}Maoriseat {c |}{res}      1,525    .1193443    .3242995          0          1
{txt}career_min~t {c |}{res}      1,608     .300995    .4588332          0          1
{txt}career_min~e {c |}{res}      3,056    .4250654    .4944338          0          1
{txt}{space 4}benefits {c |}{res}      3,056    .2840314    .4510256          0          1
{txt}diff_adopt~n {c |}{res}         15    593.6667     339.889        132       1255
{txt}{hline 13}{c +}{hline 57}
diff_rejec~n {c |}{res}         79    247.1266    304.0946         13       1567
{txt}{space 4}end_date {c |}{res}         95    19843.35    780.8027      18240      20921
{txt}{space 8}diff {c |}{res}         94    302.4255    333.5082         13       1567
{txt}benefits_l~d {c |}{res}      2,267    .2346714    .4238868          0          1
{txt}{space 6}month2 {c |}{res}      3,056     8.25949    2.843618          2         12
{txt}{hline 13}{c +}{hline 57}
{space 8}date {c |}{res}      3,056    19790.04     797.657      18164      20796
{txt}{space 4}period_1 {c |}{res}      3,056    .1390707    .3460769          0          1
{txt}{space 4}period_2 {c |}{res}      3,056    .3871073    .4871683          0          1
{txt}{space 4}period_3 {c |}{res}      3,056     .473822     .499396          0          1
{txt}{space 3}party_fac {c |}{res}      3,056     3.97644    1.829411          1          9
{txt}{hline 13}{c +}{hline 57}
{space 6}MP_fac {c |}{res}      3,056    76.24149    43.23287          1        151
{txt}benefits_c~t {c |}{res}      2,508    .2472089    .4314753          0          1
{txt}benefits_ord {c |}{res}      3,056    .2958115    .4816048          0          2
{txt}{space 2}government {c |}{res}      3,056    .2820681    .4500799          0          1
{txt}{space 1}government2 {c |}{res}      3,056    .2434555    .4292379          0          1
{txt}{hline 13}{c +}{hline 57}
{space 6}party2 {c |}{res}      3,056    3.930301     1.86115          0          7
{txt}{space 6}party3 {c |}{res}      3,056    3.093259    1.922596          0          6
{txt}{space 4}national {c |}{res}      3,056    .2434555    .4292379          0          1
{txt}committee_~r {c |}{res}      3,056    .1171466    .3216475          0          1
{txt}{space 7}whips {c |}{res}      3,056    .0556283      .22924          0          1

Following variable is string, not included:  
MP  party_x  party_y  party  
{txt}{stata `"shellout using `"descriptive_stats.tex"'"':descriptive_stats.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "descriptive_stats.txt", label"':seeout}

{com}. 
. *Appendix(Table A2-A3)
. 
. tab party2

     {txt}party2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         62        2.03        2.03
{txt}          2 {c |}{res}        609       19.93       21.96
{txt}          3 {c |}{res}      1,257       41.13       63.09
{txt}          5 {c |}{res}         65        2.13       65.22
{txt}          6 {c |}{res}        744       24.35       89.56
{txt}          7 {c |}{res}        319       10.44      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      3,056      100.00
{txt}
{com}. tab period_1

   {txt}period_1 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      2,631       86.09       86.09
{txt}          1 {c |}{res}        425       13.91      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      3,056      100.00
{txt}
{com}. tab period_2

   {txt}period_2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,873       61.29       61.29
{txt}          1 {c |}{res}      1,183       38.71      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      3,056      100.00
{txt}
{com}. tab period_3

   {txt}period_3 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,608       52.62       52.62
{txt}          1 {c |}{res}      1,448       47.38      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      3,056      100.00
{txt}
{com}. ***************************************************************************
. **Randomization Checks*****************************************************
. ***************************************************************************
. 
. *Appendix (Table A4)
. logit extracted list_constnum i.party2 committee_chair career_minister_pre period_2 period_3, cluster(MP)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-496.46569}  
Iteration 1:{space 3}log pseudolikelihood = {res:-492.96851}  
Iteration 2:{space 3}log pseudolikelihood = {res:-492.53796}  
Iteration 3:{space 3}log pseudolikelihood = {res:-492.53728}  
Iteration 4:{space 3}log pseudolikelihood = {res:-492.53728}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     3,056
{txt}{col 49}Wald chi2({res}10{txt}){col 67}= {res}     15.90
{txt}{col 49}Prob > chi2{col 67}= {res}    0.1025
{txt}Log pseudolikelihood = {res}-492.53728{txt}{col 49}Pseudo R2{col 67}= {res}    0.0079

{txt}{ralign 85:(Std. Err. adjusted for {res:151} clusters in MP)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}          extracted{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}list_constnum {c |}{col 21}{res}{space 2} -.018674{col 33}{space 2}  .206037{col 44}{space 1}   -0.09{col 53}{space 3}0.928{col 61}{space 4} -.422499{col 74}{space 3} .3851511
{txt}{space 19} {c |}
{space 13}party2 {c |}
{space 17}2  {c |}{col 21}{res}{space 2}-.1269526{col 33}{space 2} .7152895{col 44}{space 1}   -0.18{col 53}{space 3}0.859{col 61}{space 4}-1.528894{col 74}{space 3} 1.274989
{txt}{space 17}3  {c |}{col 21}{res}{space 2}-.1070696{col 33}{space 2} .6985652{col 44}{space 1}   -0.15{col 53}{space 3}0.878{col 61}{space 4}-1.476232{col 74}{space 3} 1.262093
{txt}{space 17}5  {c |}{col 21}{res}{space 2} .6356587{col 33}{space 2} .7829291{col 44}{space 1}    0.81{col 53}{space 3}0.417{col 61}{space 4}-.8988542{col 74}{space 3} 2.170172
{txt}{space 17}6  {c |}{col 21}{res}{space 2}-.0244535{col 33}{space 2} .7310334{col 44}{space 1}   -0.03{col 53}{space 3}0.973{col 61}{space 4}-1.457253{col 74}{space 3} 1.408346
{txt}{space 17}7  {c |}{col 21}{res}{space 2} .0271762{col 33}{space 2} .7487219{col 44}{space 1}    0.04{col 53}{space 3}0.971{col 61}{space 4}-1.440292{col 74}{space 3} 1.494644
{txt}{space 19} {c |}
{space 4}committee_chair {c |}{col 21}{res}{space 2}-.0639911{col 33}{space 2} .3089879{col 44}{space 1}   -0.21{col 53}{space 3}0.836{col 61}{space 4}-.6695962{col 74}{space 3}  .541614
{txt}career_minister_pre {c |}{col 21}{res}{space 2} .2693999{col 33}{space 2}  .258626{col 44}{space 1}    1.04{col 53}{space 3}0.298{col 61}{space 4}-.2374978{col 74}{space 3} .7762975
{txt}{space 11}period_2 {c |}{col 21}{res}{space 2}-.4994383{col 33}{space 2} .2591221{col 44}{space 1}   -1.93{col 53}{space 3}0.054{col 61}{space 4}-1.007308{col 74}{space 3} .0084316
{txt}{space 11}period_3 {c |}{col 21}{res}{space 2}-.4894029{col 33}{space 2} .2567592{col 44}{space 1}   -1.91{col 53}{space 3}0.057{col 61}{space 4}-.9926416{col 74}{space 3} .0138358
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-2.865722{col 33}{space 2} .6731965{col 44}{space 1}   -4.26{col 53}{space 3}0.000{col 61}{space 4}-4.185163{col 74}{space 3}-1.546281
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using regression_table_bal, tex replace label title("Member Bill") addtext(Clustered SE, YES)
{txt}{stata `"shellout using `"regression_table_bal.tex"'"':regression_table_bal.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_table_bal.txt", label"':seeout}

{com}. 
. 
. ***************************************************************************
. **Analysis Benefits - Full Sample******************************************
. ***************************************************************************
. 
. *Appendix (Table A5-A6)
. foreach y in  benefits_ord {c -(}
{txt}  2{com}. foreach x in  extracted bill_passed  {c -(}
{txt}  3{com}. 
. ologit `y' `x'  , cluster(MP)
{txt}  4{com}. outreg2 using regression_full_`y'_`x', tex replace keep(`x') label title("Private Benefits") addtext(Clustered SE, YES)
{txt}  5{com}. ologit `y' `x' list_constnum career_minister_pre committee_chair, cluster(MP)
{txt}  6{com}. outreg2 using regression_full_`y'_`x', tex append keep(`x' list_constnum career_minister_pre committee_chair) label title("Private Benefits") addtext(Clustered SE, YES)
{txt}  7{com}. ologit `y' `x' list_constnum career_minister_pre committee_chair period_2 period_3 i.party2, cluster(MP)
{txt}  8{com}. outreg2 using regression_full_`y'_`x', tex append keep( `x' list_constnum career_minister_pre committee_chair) label title("Private Benefits") addtext(Clustered SE, YES, Party FE, YES, Legislative Period FE, YES)
{txt}  9{com}. {c )-}
{txt} 10{com}. {c )-}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1973.3974}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1972.0106}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1972.0043}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1972.0043}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     3,056
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      3.01
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0829
{txt}Log pseudolikelihood = {res}-1972.0043{txt}{col 49}Pseudo R2{col 67}= {res}    0.0007

{txt}{ralign 78:(Std. Err. adjusted for {res:151} clusters in MP)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}benefits_ord{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}extracted {c |}{col 14}{res}{space 2} .3358955{col 26}{space 2} .1936873{col 37}{space 1}    1.73{col 46}{space 3}0.083{col 54}{space 4}-.0437247{col 67}{space 3} .7155156
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
       /cut1 {c |}{col 14}{res}{space 2} .9381193{col 26}{space 2} .1460409{col 54}{space 4} .6518845{col 67}{space 3} 1.224354
{txt}       /cut2 {c |}{col 14}{res}{space 2}  4.44445{col 26}{space 2} .5033605{col 54}{space 4} 3.457881{col 67}{space 3} 5.431018
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_full_benefits_ord_extracted.tex"'"':regression_full_benefits_ord_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_full_benefits_ord_extracted.txt", label"':seeout}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1973.3974}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1934.7853}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1934.3928}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1934.3928}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     3,056
{txt}{col 49}Wald chi2({res}4{txt}){col 67}= {res}     11.76
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0193
{txt}Log pseudolikelihood = {res}-1934.3928{txt}{col 49}Pseudo R2{col 67}= {res}    0.0198

{txt}{ralign 85:(Std. Err. adjusted for {res:151} clusters in MP)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}       benefits_ord{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}extracted {c |}{col 21}{res}{space 2} .3258493{col 33}{space 2} .1926329{col 44}{space 1}    1.69{col 53}{space 3}0.091{col 61}{space 4}-.0517043{col 74}{space 3} .7034029
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.3458212{col 33}{space 2} .2934322{col 44}{space 1}   -1.18{col 53}{space 3}0.239{col 61}{space 4}-.9209376{col 74}{space 3} .2292953
{txt}career_minister_pre {c |}{col 21}{res}{space 2}  .474326{col 33}{space 2} .2886072{col 44}{space 1}    1.64{col 53}{space 3}0.100{col 61}{space 4}-.0913337{col 74}{space 3} 1.039986
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .7877044{col 33}{space 2} .3492098{col 44}{space 1}    2.26{col 53}{space 3}0.024{col 61}{space 4} .1032658{col 74}{space 3} 1.472143
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
              /cut1 {c |}{col 21}{res}{space 2} 1.082099{col 33}{space 2} .2389377{col 61}{space 4}   .61379{col 74}{space 3} 1.550408
{txt}              /cut2 {c |}{col 21}{res}{space 2} 4.629241{col 33}{space 2} .5784615{col 61}{space 4} 3.495477{col 74}{space 3} 5.763005
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_full_benefits_ord_extracted.tex"'"':regression_full_benefits_ord_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_full_benefits_ord_extracted.txt", label"':seeout}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1973.3974}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1840.3183}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1834.1309}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1834.0986}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1834.0986}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     3,056
{txt}{col 49}Wald chi2({res}11{txt}){col 67}= {res}     26.64
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0052
{txt}Log pseudolikelihood = {res}-1834.0986{txt}{col 49}Pseudo R2{col 67}= {res}    0.0706

{txt}{ralign 85:(Std. Err. adjusted for {res:151} clusters in MP)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}       benefits_ord{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}extracted {c |}{col 21}{res}{space 2}  .379939{col 33}{space 2} .2018806{col 44}{space 1}    1.88{col 53}{space 3}0.060{col 61}{space 4}-.0157398{col 74}{space 3} .7756178
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.7256379{col 33}{space 2} .3129175{col 44}{space 1}   -2.32{col 53}{space 3}0.020{col 61}{space 4}-1.338945{col 74}{space 3} -.112331
{txt}career_minister_pre {c |}{col 21}{res}{space 2}  .628372{col 33}{space 2}   .47238{col 44}{space 1}    1.33{col 53}{space 3}0.183{col 61}{space 4}-.2974757{col 74}{space 3}  1.55422
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .3294838{col 33}{space 2} .3481887{col 44}{space 1}    0.95{col 53}{space 3}0.344{col 61}{space 4}-.3529535{col 74}{space 3} 1.011921
{txt}{space 11}period_2 {c |}{col 21}{res}{space 2} 1.232521{col 33}{space 2} .4298832{col 44}{space 1}    2.87{col 53}{space 3}0.004{col 61}{space 4} .3899658{col 74}{space 3} 2.075077
{txt}{space 11}period_3 {c |}{col 21}{res}{space 2} 1.366014{col 33}{space 2} .4527855{col 44}{space 1}    3.02{col 53}{space 3}0.003{col 61}{space 4} .4785702{col 74}{space 3} 2.253457
{txt}{space 19} {c |}
{space 13}party2 {c |}
{space 17}2  {c |}{col 21}{res}{space 2}-2.676763{col 33}{space 2} .9170628{col 44}{space 1}   -2.92{col 53}{space 3}0.004{col 61}{space 4}-4.474173{col 74}{space 3}-.8793526
{txt}{space 17}3  {c |}{col 21}{res}{space 2}-1.743728{col 33}{space 2} .8727454{col 44}{space 1}   -2.00{col 53}{space 3}0.046{col 61}{space 4}-3.454278{col 74}{space 3}-.0331788
{txt}{space 17}5  {c |}{col 21}{res}{space 2}-.6969581{col 33}{space 2} .9143971{col 44}{space 1}   -0.76{col 53}{space 3}0.446{col 61}{space 4}-2.489143{col 74}{space 3} 1.095227
{txt}{space 17}6  {c |}{col 21}{res}{space 2}-1.115119{col 33}{space 2}  .808743{col 44}{space 1}   -1.38{col 53}{space 3}0.168{col 61}{space 4}-2.700226{col 74}{space 3} .4699885
{txt}{space 17}7  {c |}{col 21}{res}{space 2}-1.690836{col 33}{space 2} .9403678{col 44}{space 1}   -1.80{col 53}{space 3}0.072{col 61}{space 4}-3.533924{col 74}{space 3} .1522507
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
              /cut1 {c |}{col 21}{res}{space 2} .4137326{col 33}{space 2} .8491591{col 61}{space 4}-1.250589{col 74}{space 3} 2.078054
{txt}              /cut2 {c |}{col 21}{res}{space 2}  4.06085{col 33}{space 2} .9769803{col 61}{space 4} 2.146004{col 74}{space 3} 5.975696
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_full_benefits_ord_extracted.tex"'"':regression_full_benefits_ord_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_full_benefits_ord_extracted.txt", label"':seeout}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-86.514506}  
Iteration 1:{space 3}log pseudolikelihood = {res:-82.928257}  
Iteration 2:{space 3}log pseudolikelihood = {res:-82.876935}  
Iteration 3:{space 3}log pseudolikelihood = {res:-82.876864}  
Iteration 4:{space 3}log pseudolikelihood = {res:-82.876864}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       117
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      6.29
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0121
{txt}Log pseudolikelihood = {res}-82.876864{txt}{col 49}Pseudo R2{col 67}= {res}    0.0420

{txt}{ralign 78:(Std. Err. adjusted for {res:80} clusters in MP)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}benefits_ord{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}bill_passed {c |}{col 14}{res}{space 2} 1.490233{col 26}{space 2} .5941631{col 37}{space 1}    2.51{col 46}{space 3}0.012{col 54}{space 4} .3256953{col 67}{space 3} 2.654772
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
       /cut1 {c |}{col 14}{res}{space 2} .8265823{col 26}{space 2} .2340458{col 54}{space 4}  .367861{col 67}{space 3} 1.285304
{txt}       /cut2 {c |}{col 14}{res}{space 2}  3.98981{col 26}{space 2} .5676403{col 54}{space 4} 2.877255{col 67}{space 3} 5.102364
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_full_benefits_ord_bill_passed.tex"'"':regression_full_benefits_ord_bill_passed.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_full_benefits_ord_bill_passed.txt", label"':seeout}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-86.514506}  
Iteration 1:{space 3}log pseudolikelihood = {res:-81.961939}  
Iteration 2:{space 3}log pseudolikelihood = {res:-81.903382}  
Iteration 3:{space 3}log pseudolikelihood = {res:-81.903246}  
Iteration 4:{space 3}log pseudolikelihood = {res:-81.903246}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       117
{txt}{col 49}Wald chi2({res}4{txt}){col 67}= {res}     10.03
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0399
{txt}Log pseudolikelihood = {res}-81.903246{txt}{col 49}Pseudo R2{col 67}= {res}    0.0533

{txt}{ralign 85:(Std. Err. adjusted for {res:80} clusters in MP)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}       benefits_ord{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}bill_passed {c |}{col 21}{res}{space 2} 1.354277{col 33}{space 2}  .614872{col 44}{space 1}    2.20{col 53}{space 3}0.028{col 61}{space 4} .1491498{col 74}{space 3} 2.559404
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2} .0245502{col 33}{space 2} .4876243{col 44}{space 1}    0.05{col 53}{space 3}0.960{col 61}{space 4}-.9311759{col 74}{space 3} .9802763
{txt}career_minister_pre {c |}{col 21}{res}{space 2}  .005611{col 33}{space 2} .4762359{col 44}{space 1}    0.01{col 53}{space 3}0.991{col 61}{space 4}-.9277942{col 74}{space 3} .9390162
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .8216702{col 33}{space 2} .5965923{col 44}{space 1}    1.38{col 53}{space 3}0.168{col 61}{space 4}-.3476291{col 74}{space 3}  1.99097
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
              /cut1 {c |}{col 21}{res}{space 2} .9263193{col 33}{space 2} .3394093{col 61}{space 4} .2610894{col 74}{space 3} 1.591549
{txt}              /cut2 {c |}{col 21}{res}{space 2} 4.130874{col 33}{space 2}  .687023{col 61}{space 4} 2.784334{col 74}{space 3} 5.477414
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_full_benefits_ord_bill_passed.tex"'"':regression_full_benefits_ord_bill_passed.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_full_benefits_ord_bill_passed.txt", label"':seeout}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-86.514506}  
Iteration 1:{space 3}log pseudolikelihood = {res:-71.356963}  
Iteration 2:{space 3}log pseudolikelihood = {res:-70.341595}  
Iteration 3:{space 3}log pseudolikelihood = {res:-70.334521}  
Iteration 4:{space 3}log pseudolikelihood = {res:-70.334517}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       117
{txt}{col 49}Wald chi2({res}11{txt}){col 67}= {res}     34.42
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0003
{txt}Log pseudolikelihood = {res}-70.334517{txt}{col 49}Pseudo R2{col 67}= {res}    0.1870

{txt}{ralign 85:(Std. Err. adjusted for {res:80} clusters in MP)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}       benefits_ord{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}bill_passed {c |}{col 21}{res}{space 2} 1.678468{col 33}{space 2} .7449574{col 44}{space 1}    2.25{col 53}{space 3}0.024{col 61}{space 4} .2183787{col 74}{space 3} 3.138558
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.4058611{col 33}{space 2}  .487571{col 44}{space 1}   -0.83{col 53}{space 3}0.405{col 61}{space 4}-1.361483{col 74}{space 3} .5497606
{txt}career_minister_pre {c |}{col 21}{res}{space 2} .7838466{col 33}{space 2}  .678141{col 44}{space 1}    1.16{col 53}{space 3}0.248{col 61}{space 4}-.5452853{col 74}{space 3} 2.112979
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .0664848{col 33}{space 2} .6225074{col 44}{space 1}    0.11{col 53}{space 3}0.915{col 61}{space 4}-1.153607{col 74}{space 3} 1.286577
{txt}{space 11}period_2 {c |}{col 21}{res}{space 2} 1.409507{col 33}{space 2} .8229554{col 44}{space 1}    1.71{col 53}{space 3}0.087{col 61}{space 4}-.2034559{col 74}{space 3}  3.02247
{txt}{space 11}period_3 {c |}{col 21}{res}{space 2} 2.415613{col 33}{space 2} .8757281{col 44}{space 1}    2.76{col 53}{space 3}0.006{col 61}{space 4} .6992172{col 74}{space 3} 4.132008
{txt}{space 19} {c |}
{space 13}party2 {c |}
{space 17}2  {c |}{col 21}{res}{space 2}-5.311534{col 33}{space 2} 1.219712{col 44}{space 1}   -4.35{col 53}{space 3}0.000{col 61}{space 4}-7.702125{col 74}{space 3}-2.920943
{txt}{space 17}3  {c |}{col 21}{res}{space 2}-4.924951{col 33}{space 2} 1.252415{col 44}{space 1}   -3.93{col 53}{space 3}0.000{col 61}{space 4}-7.379639{col 74}{space 3}-2.470262
{txt}{space 17}5  {c |}{col 21}{res}{space 2}-4.300731{col 33}{space 2} 1.231638{col 44}{space 1}   -3.49{col 53}{space 3}0.000{col 61}{space 4}-6.714697{col 74}{space 3}-1.886765
{txt}{space 17}6  {c |}{col 21}{res}{space 2}-3.698687{col 33}{space 2} 1.171921{col 44}{space 1}   -3.16{col 53}{space 3}0.002{col 61}{space 4}-5.995611{col 74}{space 3}-1.401763
{txt}{space 17}7  {c |}{col 21}{res}{space 2}-4.878491{col 33}{space 2} 1.571077{col 44}{space 1}   -3.11{col 53}{space 3}0.002{col 61}{space 4}-7.957744{col 74}{space 3}-1.799237
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
              /cut1 {c |}{col 21}{res}{space 2}-1.779923{col 33}{space 2} .8045509{col 61}{space 4}-3.356814{col 74}{space 3}-.2030324
{txt}              /cut2 {c |}{col 21}{res}{space 2} 1.924269{col 33}{space 2} .7012904{col 61}{space 4} .5497652{col 74}{space 3} 3.298773
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_full_benefits_ord_bill_passed.tex"'"':regression_full_benefits_ord_bill_passed.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_full_benefits_ord_bill_passed.txt", label"':seeout}

{com}. 
. 
. *Appendix (Table A7-A8) include party 3 fixed effects
. foreach y in benefits  {c -(}
{txt}  2{com}. foreach x in bill_passed  {c -(}
{txt}  3{com}. 
. logit `y' `x', cluster(MP)
{txt}  4{com}. outreg2 using regression_`y'_`x', tex replace keep(`x') label title("Private Benefits") addtext(Clustered SE, YES)
{txt}  5{com}. logit `y' `x' list_constnum career_minister_pre committee_chair, cluster(MP)
{txt}  6{com}. outreg2 using regression_`y'_`x', tex append keep(`x' list_constnum career_minister_pre committee_chair) label title("Private Benefits")  addtext(Clustered SE, YES)
{txt}  7{com}. logit `y' `x' list_constnum career_minister_pre committee_chair period_2 period_3 i.party3, cluster(MP)
{txt}  8{com}. outreg2 using regression_`y'_`x', tex append keep( `x' list_constnum career_minister_pre committee_chair) label title("Private Benefits")  addtext(Clustered SE, YES, Party FE, YES, Legislative Period FE, YES)
{txt}  9{com}. {c )-}
{txt} 10{com}. {c )-}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-75.782162}  
Iteration 1:{space 3}log pseudolikelihood = {res:-72.875738}  
Iteration 2:{space 3}log pseudolikelihood = {res:-72.863915}  
Iteration 3:{space 3}log pseudolikelihood = {res:-72.863914}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       117
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      5.84
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0157
{txt}Log pseudolikelihood = {res}-72.863914{txt}{col 49}Pseudo R2{col 67}= {res}    0.0385

{txt}{ralign 78:(Std. Err. adjusted for {res:80} clusters in MP)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    benefits{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}bill_passed {c |}{col 14}{res}{space 2} 1.325334{col 26}{space 2} .5485067{col 37}{space 1}    2.42{col 46}{space 3}0.016{col 54}{space 4} .2502803{col 67}{space 3} 2.400387
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} -.814508{col 26}{space 2} .2354935{col 37}{space 1}   -3.46{col 46}{space 3}0.001{col 54}{space 4}-1.276067{col 67}{space 3}-.3529493
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_benefits_bill_passed.tex"'"':regression_benefits_bill_passed.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_benefits_bill_passed.txt", label"':seeout}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-75.782162}  
Iteration 1:{space 3}log pseudolikelihood = {res:-71.394813}  
Iteration 2:{space 3}log pseudolikelihood = {res: -71.38742}  
Iteration 3:{space 3}log pseudolikelihood = {res: -71.38742}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       117
{txt}{col 49}Wald chi2({res}4{txt}){col 67}= {res}      8.64
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0707
{txt}Log pseudolikelihood = {res} -71.38742{txt}{col 49}Pseudo R2{col 67}= {res}    0.0580

{txt}{ralign 85:(Std. Err. adjusted for {res:80} clusters in MP)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}           benefits{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}bill_passed {c |}{col 21}{res}{space 2} 1.204509{col 33}{space 2} .5651538{col 44}{space 1}    2.13{col 53}{space 3}0.033{col 61}{space 4} .0968283{col 74}{space 3} 2.312191
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2} .0359346{col 33}{space 2}  .503638{col 44}{space 1}    0.07{col 53}{space 3}0.943{col 61}{space 4}-.9511777{col 74}{space 3} 1.023047
{txt}career_minister_pre {c |}{col 21}{res}{space 2} .0198065{col 33}{space 2} .4915946{col 44}{space 1}    0.04{col 53}{space 3}0.968{col 61}{space 4}-.9437012{col 74}{space 3} .9833143
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2}  1.05304{col 33}{space 2} .6437862{col 44}{space 1}    1.64{col 53}{space 3}0.102{col 61}{space 4}-.2087579{col 74}{space 3} 2.314838
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-.9489061{col 33}{space 2} .3295581{col 44}{space 1}   -2.88{col 53}{space 3}0.004{col 61}{space 4}-1.594828{col 74}{space 3} -.302984
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_benefits_bill_passed.tex"'"':regression_benefits_bill_passed.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_benefits_bill_passed.txt", label"':seeout}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-75.782162}  
Iteration 1:{space 3}log pseudolikelihood = {res: -64.73835}  
Iteration 2:{space 3}log pseudolikelihood = {res:-64.503661}  
Iteration 3:{space 3}log pseudolikelihood = {res:-64.502619}  
Iteration 4:{space 3}log pseudolikelihood = {res:-64.502619}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       117
{txt}{col 49}Wald chi2({res}9{txt}){col 67}= {res}     23.74
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0047
{txt}Log pseudolikelihood = {res}-64.502619{txt}{col 49}Pseudo R2{col 67}= {res}    0.1488

{txt}{ralign 85:(Std. Err. adjusted for {res:80} clusters in MP)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}           benefits{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}bill_passed {c |}{col 21}{res}{space 2} 1.205898{col 33}{space 2} .6431453{col 44}{space 1}    1.88{col 53}{space 3}0.061{col 61}{space 4}-.0546439{col 74}{space 3} 2.466439
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2} .1043154{col 33}{space 2} .5070537{col 44}{space 1}    0.21{col 53}{space 3}0.837{col 61}{space 4}-.8894916{col 74}{space 3} 1.098122
{txt}career_minister_pre {c |}{col 21}{res}{space 2} 1.001986{col 33}{space 2} .7787295{col 44}{space 1}    1.29{col 53}{space 3}0.198{col 61}{space 4} -.524296{col 74}{space 3} 2.528267
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .1226996{col 33}{space 2}  .709992{col 44}{space 1}    0.17{col 53}{space 3}0.863{col 61}{space 4}-1.268859{col 74}{space 3} 1.514258
{txt}{space 11}period_2 {c |}{col 21}{res}{space 2} .7774399{col 33}{space 2} .7796637{col 44}{space 1}    1.00{col 53}{space 3}0.319{col 61}{space 4}-.7506728{col 74}{space 3} 2.305553
{txt}{space 11}period_3 {c |}{col 21}{res}{space 2}  1.75167{col 33}{space 2} .7796829{col 44}{space 1}    2.25{col 53}{space 3}0.025{col 61}{space 4} .2235195{col 74}{space 3}  3.27982
{txt}{space 19} {c |}
{space 13}party3 {c |}
{space 17}2  {c |}{col 21}{res}{space 2}-1.235513{col 33}{space 2} .8647657{col 44}{space 1}   -1.43{col 53}{space 3}0.153{col 61}{space 4}-2.930422{col 74}{space 3} .4593971
{txt}{space 17}3  {c |}{col 21}{res}{space 2} -1.39373{col 33}{space 2} 1.041651{col 44}{space 1}   -1.34{col 53}{space 3}0.181{col 61}{space 4}-3.435328{col 74}{space 3} .6478685
{txt}{space 17}6  {c |}{col 21}{res}{space 2} .1619953{col 33}{space 2} .9334357{col 44}{space 1}    0.17{col 53}{space 3}0.862{col 61}{space 4}-1.667505{col 74}{space 3} 1.991496
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-1.697248{col 33}{space 2} 1.024042{col 44}{space 1}   -1.66{col 53}{space 3}0.097{col 61}{space 4}-3.704334{col 74}{space 3} .3098367
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_benefits_bill_passed.tex"'"':regression_benefits_bill_passed.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_benefits_bill_passed.txt", label"':seeout}

{com}. 
. foreach y in benefits_correct  {c -(}
{txt}  2{com}. foreach x in bill_passed  {c -(}
{txt}  3{com}. 
. logit `y' `x', cluster(MP)
{txt}  4{com}. outreg2 using regression_`y'_`x', tex replace keep(`x') label title("Private Benefits") addtext(Clustered SE, YES)
{txt}  5{com}. logit `y' `x' list_constnum career_minister_pre committee_chair, cluster(MP)
{txt}  6{com}. outreg2 using regression_`y'_`x', tex append keep(`x' list_constnum career_minister_pre committee_chair) label title("Private Benefits")  addtext(Clustered SE, YES)
{txt}  7{com}. logit `y' `x' list_constnum career_minister_pre committee_chair period_2 period_3 i.party2, cluster(MP)
{txt}  8{com}. outreg2 using regression_`y'_`x', tex append keep( `x' list_constnum career_minister_pre committee_chair) label title("Private Benefits")  addtext(Clustered SE, YES, Party FE, YES, Legislative Period FE, YES)
{txt}  9{com}. {c )-}
{txt} 10{com}. {c )-}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-57.305692}  
Iteration 1:{space 3}log pseudolikelihood = {res:-53.425934}  
Iteration 2:{space 3}log pseudolikelihood = {res: -53.27474}  
Iteration 3:{space 3}log pseudolikelihood = {res:-53.274606}  
Iteration 4:{space 3}log pseudolikelihood = {res:-53.274606}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       100
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      7.76
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0054
{txt}Log pseudolikelihood = {res}-53.274606{txt}{col 49}Pseudo R2{col 67}= {res}    0.0703

{txt}{ralign 82:(Std. Err. adjusted for {res:70} clusters in MP)}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}benefits_correct{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}bill_passed {c |}{col 18}{res}{space 2} 1.622794{col 30}{space 2} .5827003{col 41}{space 1}    2.78{col 50}{space 3}0.005{col 58}{space 4} .4807222{col 71}{space 3} 2.764865
{txt}{space 11}_cons {c |}{col 18}{res}{space 2}-1.371479{col 30}{space 2} .3115682{col 41}{space 1}   -4.40{col 50}{space 3}0.000{col 58}{space 4}-1.982142{col 71}{space 3}-.7608168
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_benefits_correct_bill_passed.tex"'"':regression_benefits_correct_bill_passed.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_benefits_correct_bill_passed.txt", label"':seeout}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-57.305692}  
Iteration 1:{space 3}log pseudolikelihood = {res:-52.977831}  
Iteration 2:{space 3}log pseudolikelihood = {res:-52.818814}  
Iteration 3:{space 3}log pseudolikelihood = {res:-52.818686}  
Iteration 4:{space 3}log pseudolikelihood = {res:-52.818686}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       100
{txt}{col 49}Wald chi2({res}4{txt}){col 67}= {res}      8.12
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0873
{txt}Log pseudolikelihood = {res}-52.818686{txt}{col 49}Pseudo R2{col 67}= {res}    0.0783

{txt}{ralign 85:(Std. Err. adjusted for {res:70} clusters in MP)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}   benefits_correct{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}bill_passed {c |}{col 21}{res}{space 2} 1.527018{col 33}{space 2} .5950529{col 44}{space 1}    2.57{col 53}{space 3}0.010{col 61}{space 4} .3607355{col 74}{space 3}   2.6933
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.3502585{col 33}{space 2} .6180386{col 44}{space 1}   -0.57{col 53}{space 3}0.571{col 61}{space 4}-1.561592{col 74}{space 3} .8610749
{txt}career_minister_pre {c |}{col 21}{res}{space 2} -.173362{col 33}{space 2} .6278135{col 44}{space 1}   -0.28{col 53}{space 3}0.782{col 61}{space 4}-1.403854{col 74}{space 3}  1.05713
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .5072355{col 33}{space 2} .8731548{col 44}{space 1}    0.58{col 53}{space 3}0.561{col 61}{space 4}-1.204116{col 74}{space 3} 2.218587
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.149642{col 33}{space 2} .4360063{col 44}{space 1}   -2.64{col 53}{space 3}0.008{col 61}{space 4}-2.004198{col 74}{space 3} -.295085
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_benefits_correct_bill_passed.tex"'"':regression_benefits_correct_bill_passed.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_benefits_correct_bill_passed.txt", label"':seeout}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-57.305692}  
Iteration 1:{space 3}log pseudolikelihood = {res:-47.549097}  
Iteration 2:{space 3}log pseudolikelihood = {res:-47.055597}  
Iteration 3:{space 3}log pseudolikelihood = {res:-47.051972}  
Iteration 4:{space 3}log pseudolikelihood = {res:-47.051972}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       100
{txt}{col 49}Wald chi2({res}11{txt}){col 67}= {res}     15.43
{txt}{col 49}Prob > chi2{col 67}= {res}    0.1638
{txt}Log pseudolikelihood = {res}-47.051972{txt}{col 49}Pseudo R2{col 67}= {res}    0.1789

{txt}{ralign 85:(Std. Err. adjusted for {res:70} clusters in MP)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}   benefits_correct{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}bill_passed {c |}{col 21}{res}{space 2} 1.273782{col 33}{space 2} .6407305{col 44}{space 1}    1.99{col 53}{space 3}0.047{col 61}{space 4} .0179734{col 74}{space 3} 2.529591
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}  -.72224{col 33}{space 2} .6593334{col 44}{space 1}   -1.10{col 53}{space 3}0.273{col 61}{space 4} -2.01451{col 74}{space 3} .5700298
{txt}career_minister_pre {c |}{col 21}{res}{space 2}  .966196{col 33}{space 2} 1.142342{col 44}{space 1}    0.85{col 53}{space 3}0.398{col 61}{space 4}-1.272754{col 74}{space 3} 3.205146
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2}-.2497431{col 33}{space 2} .8323936{col 44}{space 1}   -0.30{col 53}{space 3}0.764{col 61}{space 4}-1.881205{col 74}{space 3} 1.381719
{txt}{space 11}period_2 {c |}{col 21}{res}{space 2} 1.092785{col 33}{space 2} .8146502{col 44}{space 1}    1.34{col 53}{space 3}0.180{col 61}{space 4}-.5038999{col 74}{space 3}  2.68947
{txt}{space 11}period_3 {c |}{col 21}{res}{space 2} 1.405556{col 33}{space 2} .8419713{col 44}{space 1}    1.67{col 53}{space 3}0.095{col 61}{space 4} -.244677{col 74}{space 3}  3.05579
{txt}{space 19} {c |}
{space 13}party2 {c |}
{space 17}2  {c |}{col 21}{res}{space 2}-3.867212{col 33}{space 2} 2.224185{col 44}{space 1}   -1.74{col 53}{space 3}0.082{col 61}{space 4}-8.226534{col 74}{space 3} .4921108
{txt}{space 17}3  {c |}{col 21}{res}{space 2}-4.336518{col 33}{space 2}  2.38317{col 44}{space 1}   -1.82{col 53}{space 3}0.069{col 61}{space 4}-9.007444{col 74}{space 3} .3344084
{txt}{space 17}5  {c |}{col 21}{res}{space 2}-1.741515{col 33}{space 2} 2.139382{col 44}{space 1}   -0.81{col 53}{space 3}0.416{col 61}{space 4}-5.934627{col 74}{space 3} 2.451597
{txt}{space 17}6  {c |}{col 21}{res}{space 2}-2.517303{col 33}{space 2} 2.102867{col 44}{space 1}   -1.20{col 53}{space 3}0.231{col 61}{space 4}-6.638846{col 74}{space 3}  1.60424
{txt}{space 17}7  {c |}{col 21}{res}{space 2}-3.763858{col 33}{space 2} 2.370528{col 44}{space 1}   -1.59{col 53}{space 3}0.112{col 61}{space 4}-8.410007{col 74}{space 3} .8822913
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} 1.100306{col 33}{space 2} 1.899056{col 44}{space 1}    0.58{col 53}{space 3}0.562{col 61}{space 4}-2.621775{col 74}{space 3} 4.822387
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_benefits_correct_bill_passed.tex"'"':regression_benefits_correct_bill_passed.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_benefits_correct_bill_passed.txt", label"':seeout}

{com}. 
. *Appendix (Table A9)
. foreach y in  benefits_ord {c -(}
{txt}  2{com}. foreach x in  extracted {c -(}
{txt}  3{com}. 
. ologit `y' `x', cluster(MP)
{txt}  4{com}. outreg2 using regression_gov_`y'_`x' , tex replace keep(`x') label title("Private Benefits - Government") addtext(Clustered SE, YES)
{txt}  5{com}. ologit `y' `x' list_constnum career_minister_pre committee_chair, cluster(MP)
{txt}  6{com}. outreg2 using regression_gov_`y'_`x', tex append keep(`x'   list_constnum career_minister_pre committee_chair) label title("Private Benefits - Government")  addtext(Clustered SE, YES)
{txt}  7{com}. ologit `y' `x' list_constnum career_minister_pre committee_chair period_2 period_3 government, cluster(MP)
{txt}  8{com}. outreg2 using regression_gov_`y'_`x', tex append keep( `x' list_constnum career_minister_pre government committee_chair) label title("Private Benefits - Government")  addtext(Clustered SE, YES, Legislative Period FE, YES)
{txt}  9{com}. {c )-}
{txt} 10{com}. {c )-}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1973.3974}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1972.0106}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1972.0043}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1972.0043}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     3,056
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      3.01
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0829
{txt}Log pseudolikelihood = {res}-1972.0043{txt}{col 49}Pseudo R2{col 67}= {res}    0.0007

{txt}{ralign 78:(Std. Err. adjusted for {res:151} clusters in MP)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}benefits_ord{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}extracted {c |}{col 14}{res}{space 2} .3358955{col 26}{space 2} .1936873{col 37}{space 1}    1.73{col 46}{space 3}0.083{col 54}{space 4}-.0437247{col 67}{space 3} .7155156
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
       /cut1 {c |}{col 14}{res}{space 2} .9381193{col 26}{space 2} .1460409{col 54}{space 4} .6518845{col 67}{space 3} 1.224354
{txt}       /cut2 {c |}{col 14}{res}{space 2}  4.44445{col 26}{space 2} .5033605{col 54}{space 4} 3.457881{col 67}{space 3} 5.431018
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_gov_benefits_ord_extracted.tex"'"':regression_gov_benefits_ord_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_gov_benefits_ord_extracted.txt", label"':seeout}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1973.3974}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1934.7853}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1934.3928}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1934.3928}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     3,056
{txt}{col 49}Wald chi2({res}4{txt}){col 67}= {res}     11.76
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0193
{txt}Log pseudolikelihood = {res}-1934.3928{txt}{col 49}Pseudo R2{col 67}= {res}    0.0198

{txt}{ralign 85:(Std. Err. adjusted for {res:151} clusters in MP)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}       benefits_ord{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}extracted {c |}{col 21}{res}{space 2} .3258493{col 33}{space 2} .1926329{col 44}{space 1}    1.69{col 53}{space 3}0.091{col 61}{space 4}-.0517043{col 74}{space 3} .7034029
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.3458212{col 33}{space 2} .2934322{col 44}{space 1}   -1.18{col 53}{space 3}0.239{col 61}{space 4}-.9209376{col 74}{space 3} .2292953
{txt}career_minister_pre {c |}{col 21}{res}{space 2}  .474326{col 33}{space 2} .2886072{col 44}{space 1}    1.64{col 53}{space 3}0.100{col 61}{space 4}-.0913337{col 74}{space 3} 1.039986
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .7877044{col 33}{space 2} .3492098{col 44}{space 1}    2.26{col 53}{space 3}0.024{col 61}{space 4} .1032658{col 74}{space 3} 1.472143
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
              /cut1 {c |}{col 21}{res}{space 2} 1.082099{col 33}{space 2} .2389377{col 61}{space 4}   .61379{col 74}{space 3} 1.550408
{txt}              /cut2 {c |}{col 21}{res}{space 2} 4.629241{col 33}{space 2} .5784615{col 61}{space 4} 3.495477{col 74}{space 3} 5.763005
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_gov_benefits_ord_extracted.tex"'"':regression_gov_benefits_ord_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_gov_benefits_ord_extracted.txt", label"':seeout}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1973.3974}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1866.9947}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1863.9512}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1863.9406}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1863.9406}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     3,056
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     22.92
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0018
{txt}Log pseudolikelihood = {res}-1863.9406{txt}{col 49}Pseudo R2{col 67}= {res}    0.0555

{txt}{ralign 85:(Std. Err. adjusted for {res:151} clusters in MP)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}       benefits_ord{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}extracted {c |}{col 21}{res}{space 2}   .38873{col 33}{space 2} .1973787{col 44}{space 1}    1.97{col 53}{space 3}0.049{col 61}{space 4} .0018748{col 74}{space 3} .7755852
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.5759822{col 33}{space 2} .2919134{col 44}{space 1}   -1.97{col 53}{space 3}0.048{col 61}{space 4}-1.148122{col 74}{space 3}-.0038424
{txt}career_minister_pre {c |}{col 21}{res}{space 2} .9005649{col 33}{space 2} .3290222{col 44}{space 1}    2.74{col 53}{space 3}0.006{col 61}{space 4} .2556932{col 74}{space 3} 1.545437
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .1387565{col 33}{space 2} .3339082{col 44}{space 1}    0.42{col 53}{space 3}0.678{col 61}{space 4}-.5156916{col 74}{space 3} .7932046
{txt}{space 11}period_2 {c |}{col 21}{res}{space 2} 1.074898{col 33}{space 2} .4301199{col 44}{space 1}    2.50{col 53}{space 3}0.012{col 61}{space 4} .2318789{col 74}{space 3} 1.917918
{txt}{space 11}period_3 {c |}{col 21}{res}{space 2}  1.25207{col 33}{space 2} .4574734{col 44}{space 1}    2.74{col 53}{space 3}0.006{col 61}{space 4} .3554389{col 74}{space 3} 2.148702
{txt}{space 9}government {c |}{col 21}{res}{space 2} 1.020575{col 33}{space 2} .3410944{col 44}{space 1}    2.99{col 53}{space 3}0.003{col 61}{space 4} .3520423{col 74}{space 3} 1.689108
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
              /cut1 {c |}{col 21}{res}{space 2} 2.429249{col 33}{space 2} .4969337{col 61}{space 4} 1.455277{col 74}{space 3} 3.403221
{txt}              /cut2 {c |}{col 21}{res}{space 2} 6.062058{col 33}{space 2} .7248346{col 61}{space 4} 4.641409{col 74}{space 3} 7.482708
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_gov_benefits_ord_extracted.tex"'"':regression_gov_benefits_ord_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_gov_benefits_ord_extracted.txt", label"':seeout}

{com}. 
. ***************************************************************************
. **Analysis Benefits - Only Passed Bills************************************
. ***************************************************************************
. 
. drop if bill_passed == 0
{txt}(101 observations deleted)

{com}. save datasetNZballot_Aug2017_passed, replace
{txt}file datasetNZballot_Aug2017_passed.dta saved

{com}. 
. *Appendix (Table A10)
. foreach y in  benefits_ord {c -(}
{txt}  2{com}. foreach x in  extracted {c -(}
{txt}  3{com}. 
. ologit `y' `x', cluster(MP)
{txt}  4{com}. outreg2 using regression_passed_full_`y'_`x', tex replace keep(`x') label title("Private Benefits") addtext(Clustered SE, YES)
{txt}  5{com}. ologit `y' `x' list_constnum career_minister_pre committee_chair, cluster(MP)
{txt}  6{com}. outreg2 using regression_passed_full_`y'_`x', tex append keep(`x' list_constnum career_minister_pre committee_chair) label title("Private Benefits")  addtext(Clustered SE, YES)
{txt}  7{com}. ologit `y' `x' list_constnum career_minister_pre committee_chair period_2 period_3 i.party2, cluster(MP)
{txt}  8{com}. outreg2 using regression_passed_full_`y'_`x', tex append keep( `x' list_constnum career_minister_pre committee_chair) label title("Private Benefits")  addtext(Clustered SE, YES, Party FE, YES, Legislative Period FE, YES)
{txt}  9{com}. {c )-}
{txt} 10{com}. {c )-}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1906.5312}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1901.6915}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1901.4715}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1901.4713}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1901.4713}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     2,955
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      7.32
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0068
{txt}Log pseudolikelihood = {res}-1901.4713{txt}{col 49}Pseudo R2{col 67}= {res}    0.0027

{txt}{ralign 78:(Std. Err. adjusted for {res:151} clusters in MP)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}benefits_ord{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}extracted {c |}{col 14}{res}{space 2} 1.658863{col 26}{space 2} .6132403{col 37}{space 1}    2.71{col 46}{space 3}0.007{col 54}{space 4} .4569341{col 67}{space 3} 2.860792
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
       /cut1 {c |}{col 14}{res}{space 2} .9381237{col 26}{space 2} .1460412{col 54}{space 4} .6518882{col 67}{space 3} 1.224359
{txt}       /cut2 {c |}{col 14}{res}{space 2} 4.444179{col 26}{space 2} .4988041{col 54}{space 4} 3.466541{col 67}{space 3} 5.421817
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_passed_full_benefits_ord_extracted.tex"'"':regression_passed_full_benefits_ord_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_passed_full_benefits_ord_extracted.txt", label"':seeout}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1906.5312}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1864.6357}  
Iteration 2:{space 3}log pseudolikelihood = {res: -1864.062}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1864.0618}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     2,955
{txt}{col 49}Wald chi2({res}4{txt}){col 67}= {res}     16.99
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0019
{txt}Log pseudolikelihood = {res}-1864.0618{txt}{col 49}Pseudo R2{col 67}= {res}    0.0223

{txt}{ralign 85:(Std. Err. adjusted for {res:151} clusters in MP)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}       benefits_ord{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}extracted {c |}{col 21}{res}{space 2} 1.555695{col 33}{space 2} .5891847{col 44}{space 1}    2.64{col 53}{space 3}0.008{col 61}{space 4} .4009146{col 74}{space 3} 2.710476
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.3646612{col 33}{space 2} .2938277{col 44}{space 1}   -1.24{col 53}{space 3}0.215{col 61}{space 4}-.9405529{col 74}{space 3} .2112305
{txt}career_minister_pre {c |}{col 21}{res}{space 2} .5048611{col 33}{space 2} .2881397{col 44}{space 1}    1.75{col 53}{space 3}0.080{col 61}{space 4}-.0598824{col 74}{space 3} 1.069605
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .7720918{col 33}{space 2} .3509406{col 44}{space 1}    2.20{col 53}{space 3}0.028{col 61}{space 4} .0842609{col 74}{space 3} 1.459923
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
              /cut1 {c |}{col 21}{res}{space 2} 1.084904{col 33}{space 2} .2400821{col 61}{space 4} .6143513{col 74}{space 3} 1.555456
{txt}              /cut2 {c |}{col 21}{res}{space 2} 4.634059{col 33}{space 2} .5754196{col 61}{space 4} 3.506258{col 74}{space 3} 5.761861
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_passed_full_benefits_ord_extracted.tex"'"':regression_passed_full_benefits_ord_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_passed_full_benefits_ord_extracted.txt", label"':seeout}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1906.5312}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1775.4641}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1768.9414}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1768.9062}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1768.9062}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     2,955
{txt}{col 49}Wald chi2({res}11{txt}){col 67}= {res}     31.79
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0008
{txt}Log pseudolikelihood = {res}-1768.9062{txt}{col 49}Pseudo R2{col 67}= {res}    0.0722

{txt}{ralign 85:(Std. Err. adjusted for {res:151} clusters in MP)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}       benefits_ord{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}extracted {c |}{col 21}{res}{space 2}   1.5502{col 33}{space 2}    .5279{col 44}{space 1}    2.94{col 53}{space 3}0.003{col 61}{space 4} .5155349{col 74}{space 3} 2.584865
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.7495041{col 33}{space 2} .3161669{col 44}{space 1}   -2.37{col 53}{space 3}0.018{col 61}{space 4} -1.36918{col 74}{space 3}-.1298283
{txt}career_minister_pre {c |}{col 21}{res}{space 2} .6268887{col 33}{space 2} .4682154{col 44}{space 1}    1.34{col 53}{space 3}0.181{col 61}{space 4}-.2907966{col 74}{space 3} 1.544574
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .3372254{col 33}{space 2} .3471263{col 44}{space 1}    0.97{col 53}{space 3}0.331{col 61}{space 4}-.3431296{col 74}{space 3}  1.01758
{txt}{space 11}period_2 {c |}{col 21}{res}{space 2} 1.283264{col 33}{space 2} .4547363{col 44}{space 1}    2.82{col 53}{space 3}0.005{col 61}{space 4} .3919971{col 74}{space 3} 2.174531
{txt}{space 11}period_3 {c |}{col 21}{res}{space 2}  1.39803{col 33}{space 2} .4750563{col 44}{space 1}    2.94{col 53}{space 3}0.003{col 61}{space 4} .4669369{col 74}{space 3} 2.329123
{txt}{space 19} {c |}
{space 13}party2 {c |}
{space 17}2  {c |}{col 21}{res}{space 2} -2.58948{col 33}{space 2} .9207242{col 44}{space 1}   -2.81{col 53}{space 3}0.005{col 61}{space 4}-4.394067{col 74}{space 3}-.7848939
{txt}{space 17}3  {c |}{col 21}{res}{space 2}-1.624837{col 33}{space 2} .8663927{col 44}{space 1}   -1.88{col 53}{space 3}0.061{col 61}{space 4}-3.322935{col 74}{space 3} .0732617
{txt}{space 17}5  {c |}{col 21}{res}{space 2}-.5651609{col 33}{space 2} .9386955{col 44}{space 1}   -0.60{col 53}{space 3}0.547{col 61}{space 4} -2.40497{col 74}{space 3} 1.274649
{txt}{space 17}6  {c |}{col 21}{res}{space 2}-1.042211{col 33}{space 2} .8038169{col 44}{space 1}   -1.30{col 53}{space 3}0.195{col 61}{space 4}-2.617663{col 74}{space 3} .5332415
{txt}{space 17}7  {c |}{col 21}{res}{space 2}-1.600885{col 33}{space 2} .9378238{col 44}{space 1}   -1.71{col 53}{space 3}0.088{col 61}{space 4}-3.438986{col 74}{space 3} .2372155
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
              /cut1 {c |}{col 21}{res}{space 2} .5348011{col 33}{space 2} .8380332{col 61}{space 4}-1.107714{col 74}{space 3} 2.177316
{txt}              /cut2 {c |}{col 21}{res}{space 2} 4.184447{col 33}{space 2} .9719852{col 61}{space 4} 2.279391{col 74}{space 3} 6.089503
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_passed_full_benefits_ord_extracted.tex"'"':regression_passed_full_benefits_ord_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_passed_full_benefits_ord_extracted.txt", label"':seeout}

{com}. 
. *Appendix (Table A11-A12)
. foreach y in benefits benefits_correct  {c -(}
{txt}  2{com}. foreach x in extracted {c -(}
{txt}  3{com}. 
. logit `y' `x' , cluster(MP)
{txt}  4{com}. outreg2 using regression_passed_full_`y'_`x', tex replace keep(`x') label title("Private Benefits") addtext(Clustered SE, YES)
{txt}  5{com}. logit `y' `x' list_constnum career_minister_pre committee_chair, cluster(MP)
{txt}  6{com}. outreg2 using regression_passed_full_`y'_`x', tex append keep(`x' list_constnum career_minister_pre committee_chair) label title("Private Benefits")  addtext(Clustered SE, YES)
{txt}  7{com}. logit `y' `x' list_constnum career_minister_pre committee_chair period_2 period_3 i.party2, cluster(MP)
{txt}  8{com}. outreg2 using regression_passed_full_`y'_`x', tex append keep(`x' list_constnum career_minister_pre committee_chair) label title("Private Benefits")  addtext(Clustered SE, Party FE, YES, Legislative Period FE, YES)
{txt}  9{com}. {c )-}
{txt} 10{com}. {c )-}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1761.1667}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1757.2234}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1757.1144}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1757.1142}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1757.1142}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     2,955
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      7.22
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0072
{txt}Log pseudolikelihood = {res}-1757.1142{txt}{col 49}Pseudo R2{col 67}= {res}    0.0023

{txt}{ralign 78:(Std. Err. adjusted for {res:151} clusters in MP)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    benefits{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}extracted {c |}{col 14}{res}{space 2} 1.448412{col 26}{space 2} .5388642{col 37}{space 1}    2.69{col 46}{space 3}0.007{col 54}{space 4} .3922571{col 67}{space 3} 2.504566
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.9375859{col 26}{space 2}  .146083{col 37}{space 1}   -6.42{col 46}{space 3}0.000{col 54}{space 4}-1.223903{col 67}{space 3}-.6512685
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_passed_full_benefits_extracted.tex"'"':regression_passed_full_benefits_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_passed_full_benefits_extracted.txt", label"':seeout}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1761.1667}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1719.5409}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1719.1428}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1719.1427}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     2,955
{txt}{col 49}Wald chi2({res}4{txt}){col 67}= {res}     16.16
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0028
{txt}Log pseudolikelihood = {res}-1719.1427{txt}{col 49}Pseudo R2{col 67}= {res}    0.0239

{txt}{ralign 85:(Std. Err. adjusted for {res:151} clusters in MP)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}           benefits{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}extracted {c |}{col 21}{res}{space 2} 1.386916{col 33}{space 2} .5255228{col 44}{space 1}    2.64{col 53}{space 3}0.008{col 61}{space 4} .3569101{col 74}{space 3} 2.416922
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.3573229{col 33}{space 2} .2952837{col 44}{space 1}   -1.21{col 53}{space 3}0.226{col 61}{space 4}-.9360683{col 74}{space 3} .2214225
{txt}career_minister_pre {c |}{col 21}{res}{space 2} .5255165{col 33}{space 2} .2915677{col 44}{space 1}    1.80{col 53}{space 3}0.071{col 61}{space 4}-.0459457{col 74}{space 3} 1.096979
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .7715832{col 33}{space 2}  .343107{col 44}{space 1}    2.25{col 53}{space 3}0.025{col 61}{space 4} .0991058{col 74}{space 3} 1.444061
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.097071{col 33}{space 2} .2370649{col 44}{space 1}   -4.63{col 53}{space 3}0.000{col 61}{space 4} -1.56171{col 74}{space 3}-.6324326
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_passed_full_benefits_extracted.tex"'"':regression_passed_full_benefits_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_passed_full_benefits_extracted.txt", label"':seeout}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1761.1667}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1630.6423}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1624.9441}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1624.9101}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1624.9101}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     2,955
{txt}{col 49}Wald chi2({res}11{txt}){col 67}= {res}     29.28
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0021
{txt}Log pseudolikelihood = {res}-1624.9101{txt}{col 49}Pseudo R2{col 67}= {res}    0.0774

{txt}{ralign 85:(Std. Err. adjusted for {res:151} clusters in MP)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}           benefits{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}extracted {c |}{col 21}{res}{space 2} 1.381296{col 33}{space 2} .4824424{col 44}{space 1}    2.86{col 53}{space 3}0.004{col 61}{space 4} .4357259{col 74}{space 3} 2.326865
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.7349744{col 33}{space 2} .3091638{col 44}{space 1}   -2.38{col 53}{space 3}0.017{col 61}{space 4}-1.340924{col 74}{space 3}-.1290243
{txt}career_minister_pre {c |}{col 21}{res}{space 2} .7058045{col 33}{space 2}  .479108{col 44}{space 1}    1.47{col 53}{space 3}0.141{col 61}{space 4}  -.23323{col 74}{space 3} 1.644839
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .3169536{col 33}{space 2} .3361005{col 44}{space 1}    0.94{col 53}{space 3}0.346{col 61}{space 4}-.3417913{col 74}{space 3} .9756985
{txt}{space 11}period_2 {c |}{col 21}{res}{space 2} 1.265557{col 33}{space 2} .4568063{col 44}{space 1}    2.77{col 53}{space 3}0.006{col 61}{space 4} .3702331{col 74}{space 3} 2.160881
{txt}{space 11}period_3 {c |}{col 21}{res}{space 2} 1.421357{col 33}{space 2} .4791273{col 44}{space 1}    2.97{col 53}{space 3}0.003{col 61}{space 4} .4822852{col 74}{space 3}  2.36043
{txt}{space 19} {c |}
{space 13}party2 {c |}
{space 17}2  {c |}{col 21}{res}{space 2}-2.598561{col 33}{space 2}  .947941{col 44}{space 1}   -2.74{col 53}{space 3}0.006{col 61}{space 4}-4.456491{col 74}{space 3}-.7406308
{txt}{space 17}3  {c |}{col 21}{res}{space 2}-1.710749{col 33}{space 2} .9045708{col 44}{space 1}   -1.89{col 53}{space 3}0.059{col 61}{space 4}-3.483675{col 74}{space 3} .0621772
{txt}{space 17}5  {c |}{col 21}{res}{space 2}-.5510181{col 33}{space 2}  .968532{col 44}{space 1}   -0.57{col 53}{space 3}0.569{col 61}{space 4}-2.449306{col 74}{space 3}  1.34727
{txt}{space 17}6  {c |}{col 21}{res}{space 2}-1.082683{col 33}{space 2} .8367663{col 44}{space 1}   -1.29{col 53}{space 3}0.196{col 61}{space 4}-2.722714{col 74}{space 3} .5573493
{txt}{space 17}7  {c |}{col 21}{res}{space 2} -1.64408{col 33}{space 2} .9550759{col 44}{space 1}   -1.72{col 53}{space 3}0.085{col 61}{space 4}-3.515995{col 74}{space 3}  .227834
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-.5260804{col 33}{space 2} .8624957{col 44}{space 1}   -0.61{col 53}{space 3}0.542{col 61}{space 4}-2.216541{col 74}{space 3}  1.16438
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_passed_full_benefits_extracted.tex"'"':regression_passed_full_benefits_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_passed_full_benefits_extracted.txt", label"':seeout}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1359.7946}  
Iteration 1:{space 3}log pseudolikelihood = {res: -1356.409}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1356.2084}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1356.2081}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1356.2081}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     2,424
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      7.21
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0072
{txt}Log pseudolikelihood = {res}-1356.2081{txt}{col 49}Pseudo R2{col 67}= {res}    0.0026

{txt}{ralign 82:(Std. Err. adjusted for {res:149} clusters in MP)}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}benefits_correct{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}extracted {c |}{col 18}{res}{space 2} 1.367725{col 30}{space 2} .5092033{col 41}{space 1}    2.69{col 50}{space 3}0.007{col 58}{space 4} .3697046{col 71}{space 3} 2.365745
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} -1.11641{col 30}{space 2} .1615071{col 41}{space 1}   -6.91{col 50}{space 3}0.000{col 58}{space 4}-1.432958{col 71}{space 3}-.7998623
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_passed_full_benefits_correct_extracted.tex"'"':regression_passed_full_benefits_correct_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_passed_full_benefits_correct_extracted.txt", label"':seeout}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1359.7946}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1340.9131}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1340.6271}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1340.6268}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1340.6268}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     2,424
{txt}{col 49}Wald chi2({res}4{txt}){col 67}= {res}     11.57
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0209
{txt}Log pseudolikelihood = {res}-1340.6268{txt}{col 49}Pseudo R2{col 67}= {res}    0.0141

{txt}{ralign 85:(Std. Err. adjusted for {res:149} clusters in MP)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}   benefits_correct{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}extracted {c |}{col 21}{res}{space 2} 1.330843{col 33}{space 2} .5096809{col 44}{space 1}    2.61{col 53}{space 3}0.009{col 61}{space 4} .3318868{col 74}{space 3} 2.329799
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.2628637{col 33}{space 2} .3333378{col 44}{space 1}   -0.79{col 53}{space 3}0.430{col 61}{space 4}-.9161939{col 74}{space 3} .3904664
{txt}career_minister_pre {c |}{col 21}{res}{space 2}   .43426{col 33}{space 2} .3263932{col 44}{space 1}    1.33{col 53}{space 3}0.183{col 61}{space 4} -.205459{col 74}{space 3} 1.073979
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .5034887{col 33}{space 2} .3893254{col 44}{space 1}    1.29{col 53}{space 3}0.196{col 61}{space 4}-.2595752{col 74}{space 3} 1.266553
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.257492{col 33}{space 2}  .257629{col 44}{space 1}   -4.88{col 53}{space 3}0.000{col 61}{space 4}-1.762436{col 74}{space 3}-.7525485
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_passed_full_benefits_correct_extracted.tex"'"':regression_passed_full_benefits_correct_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_passed_full_benefits_correct_extracted.txt", label"':seeout}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1359.7946}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1269.4425}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1264.6066}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1264.5923}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1264.5923}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     2,424
{txt}{col 49}Wald chi2({res}11{txt}){col 67}= {res}     25.40
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0080
{txt}Log pseudolikelihood = {res}-1264.5923{txt}{col 49}Pseudo R2{col 67}= {res}    0.0700

{txt}{ralign 85:(Std. Err. adjusted for {res:149} clusters in MP)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}   benefits_correct{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}extracted {c |}{col 21}{res}{space 2} 1.126796{col 33}{space 2} .4817687{col 44}{space 1}    2.34{col 53}{space 3}0.019{col 61}{space 4} .1825468{col 74}{space 3} 2.071046
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.7337855{col 33}{space 2} .3652334{col 44}{space 1}   -2.01{col 53}{space 3}0.045{col 61}{space 4} -1.44963{col 74}{space 3}-.0179412
{txt}career_minister_pre {c |}{col 21}{res}{space 2} .3448057{col 33}{space 2}  .432379{col 44}{space 1}    0.80{col 53}{space 3}0.425{col 61}{space 4}-.5026416{col 74}{space 3} 1.192253
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .0270324{col 33}{space 2} .3526256{col 44}{space 1}    0.08{col 53}{space 3}0.939{col 61}{space 4}-.6641011{col 74}{space 3} .7181659
{txt}{space 11}period_2 {c |}{col 21}{res}{space 2}  .427889{col 33}{space 2} .3724117{col 44}{space 1}    1.15{col 53}{space 3}0.251{col 61}{space 4}-.3020245{col 74}{space 3} 1.157802
{txt}{space 11}period_3 {c |}{col 21}{res}{space 2} .9584025{col 33}{space 2} .4114371{col 44}{space 1}    2.33{col 53}{space 3}0.020{col 61}{space 4} .1520005{col 74}{space 3} 1.764804
{txt}{space 19} {c |}
{space 13}party2 {c |}
{space 17}2  {c |}{col 21}{res}{space 2}-1.881253{col 33}{space 2} 1.002008{col 44}{space 1}   -1.88{col 53}{space 3}0.060{col 61}{space 4}-3.845153{col 74}{space 3} .0826459
{txt}{space 17}3  {c |}{col 21}{res}{space 2}-.4486968{col 33}{space 2} .9368323{col 44}{space 1}   -0.48{col 53}{space 3}0.632{col 61}{space 4}-2.284854{col 74}{space 3} 1.387461
{txt}{space 17}5  {c |}{col 21}{res}{space 2} .7446561{col 33}{space 2} 1.141462{col 44}{space 1}    0.65{col 53}{space 3}0.514{col 61}{space 4}-1.492568{col 74}{space 3}  2.98188
{txt}{space 17}6  {c |}{col 21}{res}{space 2}-.0095265{col 33}{space 2} .9162644{col 44}{space 1}   -0.01{col 53}{space 3}0.992{col 61}{space 4}-1.805372{col 74}{space 3} 1.786319
{txt}{space 17}7  {c |}{col 21}{res}{space 2}-.8681506{col 33}{space 2} 1.111077{col 44}{space 1}   -0.78{col 53}{space 3}0.435{col 61}{space 4}-3.045822{col 74}{space 3} 1.309521
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-.9442194{col 33}{space 2} .8752423{col 44}{space 1}   -1.08{col 53}{space 3}0.281{col 61}{space 4}-2.659663{col 74}{space 3} .7712239
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_passed_full_benefits_correct_extracted.tex"'"':regression_passed_full_benefits_correct_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_passed_full_benefits_correct_extracted.txt", label"':seeout}

{com}. 
. *Appendix (Table A13-A14)
. foreach y in  benefits benefits_correct  {c -(}
{txt}  2{com}. foreach x in  extracted {c -(}
{txt}  3{com}. 
. firthlogit `y' `x' 
{txt}  4{com}. outreg2 using regression_firthlogit_full_`y'_`x', tex replace keep(`x') label title("Private Benefits") addtext()
{txt}  5{com}. firthlogit `y' `x' list_constnum career_minister_pre committee_chair
{txt}  6{com}. outreg2 using regression_firthlogit_full_`y'_`x', tex append keep(`x' list_constnum    career_minister_pre committee_chair) label title("Private Benefits")  addtext()
{txt}  7{com}. firthlogit `y' `x' list_constnum career_minister_pre committee_chair period_2 period_3 i.party2
{txt}  8{com}. outreg2 using regression_firthlogit_full_`y'_`x', tex append keep( `x'  list_constnum  career_minister_pre committee_chair) label title("Private Benefits")  addtext(Party FE, YES, Legislative Period FE, YES)
{txt}  9{com}. {c )-}
{txt} 10{com}. {c )-}
{res}
{txt}initial:{col 16}penalized log likelihood = {res: -1757.382}
rescale:{col 16}penalized log likelihood = {res: -1757.382}
{res}{txt}Iteration 0:{space 3}penalized log likelihood = {res: -1757.382}  
Iteration 1:{space 3}penalized log likelihood = {res:-1753.3689}  
Iteration 2:{space 3}penalized log likelihood = {res:-1753.2578}  
Iteration 3:{space 3}penalized log likelihood = {res:-1753.2576}  
{res}
{txt}{col 49}Number of obs{col 67}= {res}     2,955
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      8.00
{txt}Penalized log likelihood = {res}-1753.2576{txt}{col 49}Prob > chi2{col 67}= {res}    0.0047

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    benefits{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}extracted {c |}{col 14}{res}{space 2} 1.416782{col 26}{space 2} .5007652{col 37}{space 1}    2.83{col 46}{space 3}0.005{col 54}{space 4} .4353005{col 67}{space 3} 2.398264
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.9372183{col 26}{space 2} .0410101{col 37}{space 1}  -22.85{col 46}{space 3}0.000{col 54}{space 4}-1.017597{col 67}{space 3}-.8568399
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_firthlogit_full_benefits_extracted.tex"'"':regression_firthlogit_full_benefits_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_firthlogit_full_benefits_extracted.txt", label"':seeout}
{res}
{txt}initial:{col 16}penalized log likelihood = {res:-1750.3894}
rescale:{col 16}penalized log likelihood = {res:-1750.3894}
{res}{txt}Iteration 0:{space 3}penalized log likelihood = {res:-1750.3894}  
Iteration 1:{space 3}penalized log likelihood = {res:-1708.6867}  
Iteration 2:{space 3}penalized log likelihood = {res:-1708.2893}  
Iteration 3:{space 3}penalized log likelihood = {res:-1708.2892}  
{res}
{txt}{col 49}Number of obs{col 67}= {res}     2,955
{txt}{col 49}Wald chi2({res}4{txt}){col 67}= {res}     82.28
{txt}Penalized log likelihood = {res}-1708.2892{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           benefits{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}extracted {c |}{col 21}{res}{space 2} 1.355092{col 33}{space 2} .5109027{col 44}{space 1}    2.65{col 53}{space 3}0.008{col 61}{space 4} .3537409{col 74}{space 3} 2.356443
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.3563927{col 33}{space 2} .0897948{col 44}{space 1}   -3.97{col 53}{space 3}0.000{col 61}{space 4}-.5323872{col 74}{space 3}-.1803982
{txt}career_minister_pre {c |}{col 21}{res}{space 2}  .524452{col 33}{space 2} .0880119{col 44}{space 1}    5.96{col 53}{space 3}0.000{col 61}{space 4} .3519518{col 74}{space 3} .6969523
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .7708664{col 33}{space 2} .1211183{col 44}{space 1}    6.36{col 53}{space 3}0.000{col 61}{space 4} .5334789{col 74}{space 3} 1.008254
{txt}{space 14}_cons {c |}{col 21}{res}{space 2} -1.09569{col 33}{space 2} .0644819{col 44}{space 1}  -16.99{col 53}{space 3}0.000{col 61}{space 4}-1.222073{col 74}{space 3}-.9693083
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_firthlogit_full_benefits_extracted.tex"'"':regression_firthlogit_full_benefits_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_firthlogit_full_benefits_extracted.txt", label"':seeout}
{res}
{txt}initial:{col 16}penalized log likelihood = {res:-1737.7201}
rescale:{col 16}penalized log likelihood = {res:-1737.7201}
{res}{txt}Iteration 0:{space 3}penalized log likelihood = {res:-1737.7201}  
Iteration 1:{space 3}penalized log likelihood = {res:-1607.7302}  
Iteration 2:{space 3}penalized log likelihood = {res:-1602.0887}  
Iteration 3:{space 3}penalized log likelihood = {res:-1602.0552}  
Iteration 4:{space 3}penalized log likelihood = {res:-1602.0552}  
{res}
{txt}{col 49}Number of obs{col 67}= {res}     2,955
{txt}{col 49}Wald chi2({res}11{txt}){col 67}= {res}    221.24
{txt}Penalized log likelihood = {res}-1602.0552{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           benefits{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}extracted {c |}{col 21}{res}{space 2}  1.34482{col 33}{space 2} .5363815{col 44}{space 1}    2.51{col 53}{space 3}0.012{col 61}{space 4} .2935315{col 74}{space 3} 2.396108
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.7312904{col 33}{space 2} .1041503{col 44}{space 1}   -7.02{col 53}{space 3}0.000{col 61}{space 4}-.9354213{col 74}{space 3}-.5271595
{txt}career_minister_pre {c |}{col 21}{res}{space 2} .6999726{col 33}{space 2} .1501346{col 44}{space 1}    4.66{col 53}{space 3}0.000{col 61}{space 4} .4057142{col 74}{space 3} .9942309
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .3171823{col 33}{space 2} .1460952{col 44}{space 1}    2.17{col 53}{space 3}0.030{col 61}{space 4} .0308409{col 74}{space 3} .6035237
{txt}{space 11}period_2 {c |}{col 21}{res}{space 2} 1.250676{col 33}{space 2} .1732975{col 44}{space 1}    7.22{col 53}{space 3}0.000{col 61}{space 4} .9110194{col 74}{space 3} 1.590333
{txt}{space 11}period_3 {c |}{col 21}{res}{space 2} 1.405298{col 33}{space 2} .1746747{col 44}{space 1}    8.05{col 53}{space 3}0.000{col 61}{space 4} 1.062941{col 74}{space 3} 1.747654
{txt}{space 19} {c |}
{space 13}party2 {c |}
{space 17}2  {c |}{col 21}{res}{space 2}-2.587719{col 33}{space 2} .3231509{col 44}{space 1}   -8.01{col 53}{space 3}0.000{col 61}{space 4}-3.221083{col 74}{space 3}-1.954355
{txt}{space 17}3  {c |}{col 21}{res}{space 2}-1.701898{col 33}{space 2} .3181899{col 44}{space 1}   -5.35{col 53}{space 3}0.000{col 61}{space 4}-2.325539{col 74}{space 3}-1.078257
{txt}{space 17}5  {c |}{col 21}{res}{space 2}-.5402412{col 33}{space 2}  .404514{col 44}{space 1}   -1.34{col 53}{space 3}0.182{col 61}{space 4}-1.333074{col 74}{space 3} .2525916
{txt}{space 17}6  {c |}{col 21}{res}{space 2}-1.078053{col 33}{space 2} .3037804{col 44}{space 1}   -3.55{col 53}{space 3}0.000{col 61}{space 4}-1.673452{col 74}{space 3}-.4826548
{txt}{space 17}7  {c |}{col 21}{res}{space 2}-1.636046{col 33}{space 2}   .32577{col 44}{space 1}   -5.02{col 53}{space 3}0.000{col 61}{space 4}-2.274543{col 74}{space 3}-.9975485
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-.5151042{col 33}{space 2} .2850319{col 44}{space 1}   -1.81{col 53}{space 3}0.071{col 61}{space 4}-1.073756{col 74}{space 3} .0435481
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_firthlogit_full_benefits_extracted.tex"'"':regression_firthlogit_full_benefits_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_firthlogit_full_benefits_extracted.txt", label"':seeout}
{res}
{txt}initial:{col 16}penalized log likelihood = {res:-1356.1924}
rescale:{col 16}penalized log likelihood = {res:-1356.1924}
{res}{txt}Iteration 0:{space 3}penalized log likelihood = {res:-1356.1924}  
Iteration 1:{space 3}penalized log likelihood = {res:-1352.6766}  
Iteration 2:{space 3}penalized log likelihood = {res:-1352.4707}  
Iteration 3:{space 3}penalized log likelihood = {res:-1352.4705}  
Iteration 4:{space 3}penalized log likelihood = {res:-1352.4705}  
{res}
{txt}{col 49}Number of obs{col 67}= {res}     2,424
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      7.59
{txt}Penalized log likelihood = {res}-1352.4705{txt}{col 49}Prob > chi2{col 67}= {res}    0.0059

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}benefits_c~t{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}extracted {c |}{col 14}{res}{space 2} 1.352234{col 26}{space 2} .4907442{col 37}{space 1}    2.76{col 46}{space 3}0.006{col 54}{space 4} .3903927{col 67}{space 3} 2.314074
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-1.115845{col 26}{space 2} .0472568{col 37}{space 1}  -23.61{col 46}{space 3}0.000{col 54}{space 4}-1.208466{col 67}{space 3}-1.023223
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_firthlogit_full_benefits_correct_extracted.tex"'"':regression_firthlogit_full_benefits_correct_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_firthlogit_full_benefits_correct_extracted.txt", label"':seeout}
{res}
{txt}initial:{col 16}penalized log likelihood = {res:-1349.6296}
rescale:{col 16}penalized log likelihood = {res:-1349.6296}
{res}{txt}Iteration 0:{space 3}penalized log likelihood = {res:-1349.6296}  
Iteration 1:{space 3}penalized log likelihood = {res:-1330.5866}  
Iteration 2:{space 3}penalized log likelihood = {res: -1330.295}  
Iteration 3:{space 3}penalized log likelihood = {res:-1330.2948}  
Iteration 4:{space 3}penalized log likelihood = {res:-1330.2948}  
{res}
{txt}{col 49}Number of obs{col 67}= {res}     2,424
{txt}{col 49}Wald chi2({res}4{txt}){col 67}= {res}     38.48
{txt}Penalized log likelihood = {res}-1330.2948{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   benefits_correct{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}extracted {c |}{col 21}{res}{space 2} 1.313541{col 33}{space 2} .4965084{col 44}{space 1}    2.65{col 53}{space 3}0.008{col 61}{space 4}  .340402{col 74}{space 3} 2.286679
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.2619575{col 33}{space 2} .1029397{col 44}{space 1}   -2.54{col 53}{space 3}0.011{col 61}{space 4}-.4637156{col 74}{space 3}-.0601994
{txt}career_minister_pre {c |}{col 21}{res}{space 2} .4331627{col 33}{space 2} .1004593{col 44}{space 1}    4.31{col 53}{space 3}0.000{col 61}{space 4}  .236266{col 74}{space 3} .6300593
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .5044164{col 33}{space 2} .1406519{col 44}{space 1}    3.59{col 53}{space 3}0.000{col 61}{space 4} .2287438{col 74}{space 3} .7800891
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.255451{col 33}{space 2} .0742442{col 44}{space 1}  -16.91{col 53}{space 3}0.000{col 61}{space 4}-1.400967{col 74}{space 3}-1.109935
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_firthlogit_full_benefits_correct_extracted.tex"'"':regression_firthlogit_full_benefits_correct_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_firthlogit_full_benefits_correct_extracted.txt", label"':seeout}
{res}
{txt}initial:{col 16}penalized log likelihood = {res:-1337.8613}
rescale:{col 16}penalized log likelihood = {res:-1337.8613}
{res}{txt}Iteration 0:{space 3}penalized log likelihood = {res:-1337.8613}  
Iteration 1:{space 3}penalized log likelihood = {res:-1247.6711}  
Iteration 2:{space 3}penalized log likelihood = {res:-1242.9084}  
Iteration 3:{space 3}penalized log likelihood = {res:-1242.8945}  
Iteration 4:{space 3}penalized log likelihood = {res:-1242.8945}  
{res}
{txt}{col 49}Number of obs{col 67}= {res}     2,424
{txt}{col 49}Wald chi2({res}11{txt}){col 67}= {res}    156.78
{txt}Penalized log likelihood = {res}-1242.8945{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   benefits_correct{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}extracted {c |}{col 21}{res}{space 2} 1.105959{col 33}{space 2} .5065773{col 44}{space 1}    2.18{col 53}{space 3}0.029{col 61}{space 4} .1130859{col 74}{space 3} 2.098832
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.7290696{col 33}{space 2} .1160897{col 44}{space 1}   -6.28{col 53}{space 3}0.000{col 61}{space 4}-.9566013{col 74}{space 3} -.501538
{txt}career_minister_pre {c |}{col 21}{res}{space 2} .3416167{col 33}{space 2} .1627756{col 44}{space 1}    2.10{col 53}{space 3}0.036{col 61}{space 4} .0225823{col 74}{space 3} .6606511
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .0295412{col 33}{space 2} .1687677{col 44}{space 1}    0.18{col 53}{space 3}0.861{col 61}{space 4}-.3012375{col 74}{space 3} .3603198
{txt}{space 11}period_2 {c |}{col 21}{res}{space 2}  .420901{col 33}{space 2} .1597614{col 44}{space 1}    2.63{col 53}{space 3}0.008{col 61}{space 4} .1077743{col 74}{space 3} .7340277
{txt}{space 11}period_3 {c |}{col 21}{res}{space 2} .9483322{col 33}{space 2}  .167261{col 44}{space 1}    5.67{col 53}{space 3}0.000{col 61}{space 4} .6205068{col 74}{space 3} 1.276158
{txt}{space 19} {c |}
{space 13}party2 {c |}
{space 17}2  {c |}{col 21}{res}{space 2}-1.889656{col 33}{space 2} .3731584{col 44}{space 1}   -5.06{col 53}{space 3}0.000{col 61}{space 4}-2.621033{col 74}{space 3}-1.158279
{txt}{space 17}3  {c |}{col 21}{res}{space 2}-.4657079{col 33}{space 2} .3609235{col 44}{space 1}   -1.29{col 53}{space 3}0.197{col 61}{space 4}-1.173105{col 74}{space 3} .2416892
{txt}{space 17}5  {c |}{col 21}{res}{space 2} .7267341{col 33}{space 2} .4333983{col 44}{space 1}    1.68{col 53}{space 3}0.094{col 61}{space 4}-.1227111{col 74}{space 3} 1.576179
{txt}{space 17}6  {c |}{col 21}{res}{space 2}-.0287339{col 33}{space 2} .3485882{col 44}{space 1}   -0.08{col 53}{space 3}0.934{col 61}{space 4}-.7119542{col 74}{space 3} .6544864
{txt}{space 17}7  {c |}{col 21}{res}{space 2}-.8803605{col 33}{space 2}  .378077{col 44}{space 1}   -2.33{col 53}{space 3}0.020{col 61}{space 4}-1.621378{col 74}{space 3}-.1393432
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-.9164801{col 33}{space 2} .3267659{col 44}{space 1}   -2.80{col 53}{space 3}0.005{col 61}{space 4}-1.556929{col 74}{space 3}-.2760308
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_firthlogit_full_benefits_correct_extracted.tex"'"':regression_firthlogit_full_benefits_correct_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_firthlogit_full_benefits_correct_extracted.txt", label"':seeout}

{com}. 
. ***************************************************************************
. **Analysis Benefits - Only Rejected Bills**********************************
. ***************************************************************************
. use datasetNZballot_Aug2017_prepared, clear
{txt}(Written by R.              )

{com}. 
. drop if bill_passed == 1
{txt}(16 observations deleted)

{com}. save datasetNZballot_Aug2017_rejected, replace
{txt}file datasetNZballot_Aug2017_rejected.dta saved

{com}. 
. *Appendix (Table A15)
. foreach y in benefits_ord {c -(}
{txt}  2{com}. foreach x in extracted {c -(}
{txt}  3{com}. ologit `y' `x',cluster(MP)
{txt}  4{com}. outreg2 using regression_rejected_full_`y'_`x' , tex replace keep(`x') label title("Private Benefits") addtext(Clustered SE, YES)
{txt}  5{com}. ologit `y' `x' list_constnum career_minister_pre committee_chair, cluster(MP)
{txt}  6{com}. outreg2 using regression_rejected_full_`y'_`x', tex append  keep(`x'   list_constnum career_minister_pre committee_chair) label title("Private Benefits")  addtext(Clustered SE, YES)
{txt}  7{com}. ologit `y' `x' list_constnum career_minister_pre committee_chair period_2 period_3 i.party2, cluster(MP)
{txt}  8{com}. outreg2 using regression_rejected_full_`y'_`x', tex append keep( `x' list_constnum career_minister_pre committee_chair) label title("Private Benefits")  addtext(Clustered SE, YES, Party FE, YES, Legislative Period FE, YES)
{txt}  9{com}. {c )-}
{txt} 10{com}. {c )-}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1952.0403}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1951.8946}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1951.8945}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     3,040
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      0.34
{txt}{col 49}Prob > chi2{col 67}= {res}    0.5575
{txt}Log pseudolikelihood = {res}-1951.8945{txt}{col 49}Pseudo R2{col 67}= {res}    0.0001

{txt}{ralign 78:(Std. Err. adjusted for {res:151} clusters in MP)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}benefits_ord{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}extracted {c |}{col 14}{res}{space 2} .1191194{col 26}{space 2} .2030999{col 37}{space 1}    0.59{col 46}{space 3}0.558{col 54}{space 4} -.278949{col 67}{space 3} .5171879
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
       /cut1 {c |}{col 14}{res}{space 2} .9374587{col 26}{space 2} .1460793{col 54}{space 4} .6511484{col 67}{space 3} 1.223769
{txt}       /cut2 {c |}{col 14}{res}{space 2} 4.486205{col 26}{space 2} .5057749{col 54}{space 4} 3.494904{col 67}{space 3} 5.477505
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_rejected_full_benefits_ord_extracted.tex"'"':regression_rejected_full_benefits_ord_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_rejected_full_benefits_ord_extracted.txt", label"':seeout}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1952.0403}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1915.7366}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1915.3665}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1915.3665}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     3,040
{txt}{col 49}Wald chi2({res}4{txt}){col 67}= {res}      8.10
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0879
{txt}Log pseudolikelihood = {res}-1915.3665{txt}{col 49}Pseudo R2{col 67}= {res}    0.0188

{txt}{ralign 85:(Std. Err. adjusted for {res:151} clusters in MP)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}       benefits_ord{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}extracted {c |}{col 21}{res}{space 2} .1225554{col 33}{space 2} .2068385{col 44}{space 1}    0.59{col 53}{space 3}0.554{col 61}{space 4}-.2828405{col 74}{space 3} .5279513
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.3416909{col 33}{space 2} .2947672{col 44}{space 1}   -1.16{col 53}{space 3}0.246{col 61}{space 4}-.9194239{col 74}{space 3} .2360422
{txt}career_minister_pre {c |}{col 21}{res}{space 2} .4687779{col 33}{space 2} .2901777{col 44}{space 1}    1.62{col 53}{space 3}0.106{col 61}{space 4}-.0999599{col 74}{space 3} 1.037516
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .7827696{col 33}{space 2} .3543324{col 44}{space 1}    2.21{col 53}{space 3}0.027{col 61}{space 4} .0882909{col 74}{space 3} 1.477248
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
              /cut1 {c |}{col 21}{res}{space 2} 1.080098{col 33}{space 2} .2392862{col 61}{space 4} .6111052{col 74}{space 3}  1.54909
{txt}              /cut2 {c |}{col 21}{res}{space 2} 4.668466{col 33}{space 2} .5824809{col 61}{space 4} 3.526824{col 74}{space 3} 5.810107
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_rejected_full_benefits_ord_extracted.tex"'"':regression_rejected_full_benefits_ord_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_rejected_full_benefits_ord_extracted.txt", label"':seeout}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1952.0403}  
Iteration 1:{space 3}log pseudolikelihood = {res: -1823.761}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1817.8319}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1817.8016}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1817.8016}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     3,040
{txt}{col 49}Wald chi2({res}11{txt}){col 67}= {res}     23.53
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0149
{txt}Log pseudolikelihood = {res}-1817.8016{txt}{col 49}Pseudo R2{col 67}= {res}    0.0688

{txt}{ralign 85:(Std. Err. adjusted for {res:151} clusters in MP)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}       benefits_ord{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}extracted {c |}{col 21}{res}{space 2} .1784283{col 33}{space 2} .2146303{col 44}{space 1}    0.83{col 53}{space 3}0.406{col 61}{space 4}-.2422393{col 74}{space 3} .5990959
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.7221553{col 33}{space 2} .3142618{col 44}{space 1}   -2.30{col 53}{space 3}0.022{col 61}{space 4}-1.338097{col 74}{space 3}-.1062136
{txt}career_minister_pre {c |}{col 21}{res}{space 2}  .615323{col 33}{space 2} .4773159{col 44}{space 1}    1.29{col 53}{space 3}0.197{col 61}{space 4} -.320199{col 74}{space 3} 1.550845
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .3412693{col 33}{space 2} .3498656{col 44}{space 1}    0.98{col 53}{space 3}0.329{col 61}{space 4}-.3444547{col 74}{space 3} 1.026993
{txt}{space 11}period_2 {c |}{col 21}{res}{space 2} 1.211802{col 33}{space 2} .4334645{col 44}{space 1}    2.80{col 53}{space 3}0.005{col 61}{space 4}  .362227{col 74}{space 3} 2.061376
{txt}{space 11}period_3 {c |}{col 21}{res}{space 2} 1.350837{col 33}{space 2} .4570869{col 44}{space 1}    2.96{col 53}{space 3}0.003{col 61}{space 4} .4549633{col 74}{space 3} 2.246711
{txt}{space 19} {c |}
{space 13}party2 {c |}
{space 17}2  {c |}{col 21}{res}{space 2}-2.669627{col 33}{space 2} .9200539{col 44}{space 1}   -2.90{col 53}{space 3}0.004{col 61}{space 4}  -4.4729{col 74}{space 3}-.8663548
{txt}{space 17}3  {c |}{col 21}{res}{space 2}-1.732017{col 33}{space 2} .8782042{col 44}{space 1}   -1.97{col 53}{space 3}0.049{col 61}{space 4}-3.453266{col 74}{space 3}-.0107685
{txt}{space 17}5  {c |}{col 21}{res}{space 2}-.6738356{col 33}{space 2} .9157505{col 44}{space 1}   -0.74{col 53}{space 3}0.462{col 61}{space 4}-2.468674{col 74}{space 3} 1.121002
{txt}{space 17}6  {c |}{col 21}{res}{space 2}-1.133319{col 33}{space 2}  .812749{col 44}{space 1}   -1.39{col 53}{space 3}0.163{col 61}{space 4}-2.726278{col 74}{space 3} .4596396
{txt}{space 17}7  {c |}{col 21}{res}{space 2}-1.678456{col 33}{space 2} .9436007{col 44}{space 1}   -1.78{col 53}{space 3}0.075{col 61}{space 4} -3.52788{col 74}{space 3}  .170967
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
              /cut1 {c |}{col 21}{res}{space 2} .3965461{col 33}{space 2} .8550682{col 61}{space 4}-1.279357{col 74}{space 3} 2.072449
{txt}              /cut2 {c |}{col 21}{res}{space 2} 4.081561{col 33}{space 2} .9849488{col 61}{space 4} 2.151097{col 74}{space 3} 6.012025
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_rejected_full_benefits_ord_extracted.tex"'"':regression_rejected_full_benefits_ord_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_rejected_full_benefits_ord_extracted.txt", label"':seeout}

{com}. 
. *Appendix (Table A16-A17)
. foreach y in benefits benefits_correct  {c -(}
{txt}  2{com}. foreach x in extracted {c -(}
{txt}  3{com}. logit `y' `x', cluster(MP)
{txt}  4{com}. outreg2 using regression_rejected_full_`y'_`x' , tex replace keep(`x') label title("Private Benefits")  addtext(Clustered SE, YES)
{txt}  5{com}. logit `y' `x' list_constnum career_minister_pre committee_chair, cluster(MP)
{txt}  6{com}. outreg2 using regression_rejected_full_`y'_`x', tex append keep(`x' list_constnum    career_minister_pre committee_chair) label title("Private Benefits")  addtext(Clustered SE, YES)
{txt}  7{com}. logit `y' `x' list_constnum career_minister_pre committee_chair period_2 period_3 i.party2, cluster(MP)
{txt}  8{com}. outreg2 using regression_rejected_full_`y'_`x', tex append keep( `x' list_constnum career_minister_pre committee_chair) label title("Private Benefits")  addtext(Clustered SE, YES, Party FE, YES, Legislative Period FE, YES)
{txt}  9{com}. {c )-}
{txt} 10{com}. {c )-}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1808.9628}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1808.8082}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1808.8081}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     3,040
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      0.36
{txt}{col 49}Prob > chi2{col 67}= {res}    0.5484
{txt}Log pseudolikelihood = {res}-1808.8081{txt}{col 49}Pseudo R2{col 67}= {res}    0.0001

{txt}{ralign 78:(Std. Err. adjusted for {res:151} clusters in MP)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    benefits{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}extracted {c |}{col 14}{res}{space 2} .1230797{col 26}{space 2} .2050641{col 37}{space 1}    0.60{col 46}{space 3}0.548{col 54}{space 4}-.2788386{col 67}{space 3} .5249979
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.9375859{col 26}{space 2}  .146083{col 37}{space 1}   -6.42{col 46}{space 3}0.000{col 54}{space 4}-1.223903{col 67}{space 3}-.6512685
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_rejected_full_benefits_extracted.tex"'"':regression_rejected_full_benefits_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_rejected_full_benefits_extracted.txt", label"':seeout}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1808.9628}  
Iteration 1:{space 3}log pseudolikelihood = {res: -1772.413}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1772.0891}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1772.0891}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     3,040
{txt}{col 49}Wald chi2({res}4{txt}){col 67}= {res}      7.89
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0957
{txt}Log pseudolikelihood = {res}-1772.0891{txt}{col 49}Pseudo R2{col 67}= {res}    0.0204

{txt}{ralign 85:(Std. Err. adjusted for {res:151} clusters in MP)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}           benefits{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}extracted {c |}{col 21}{res}{space 2} .1273305{col 33}{space 2} .2093758{col 44}{space 1}    0.61{col 53}{space 3}0.543{col 61}{space 4}-.2830386{col 74}{space 3} .5376996
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.3321282{col 33}{space 2} .2961445{col 44}{space 1}   -1.12{col 53}{space 3}0.262{col 61}{space 4}-.9125608{col 74}{space 3} .2483043
{txt}career_minister_pre {c |}{col 21}{res}{space 2} .4881089{col 33}{space 2} .2934308{col 44}{space 1}    1.66{col 53}{space 3}0.096{col 61}{space 4}-.0870048{col 74}{space 3} 1.063223
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .7762442{col 33}{space 2} .3477984{col 44}{space 1}    2.23{col 53}{space 3}0.026{col 61}{space 4} .0945718{col 74}{space 3} 1.457917
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.092613{col 33}{space 2} .2363975{col 44}{space 1}   -4.62{col 53}{space 3}0.000{col 61}{space 4}-1.555943{col 74}{space 3}-.6292819
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_rejected_full_benefits_extracted.tex"'"':regression_rejected_full_benefits_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_rejected_full_benefits_extracted.txt", label"':seeout}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1808.9628}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1680.6461}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1675.1316}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1675.1024}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1675.1024}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     3,040
{txt}{col 49}Wald chi2({res}11{txt}){col 67}= {res}     22.87
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0184
{txt}Log pseudolikelihood = {res}-1675.1024{txt}{col 49}Pseudo R2{col 67}= {res}    0.0740

{txt}{ralign 85:(Std. Err. adjusted for {res:151} clusters in MP)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}           benefits{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}extracted {c |}{col 21}{res}{space 2} .1849866{col 33}{space 2}   .21719{col 44}{space 1}    0.85{col 53}{space 3}0.394{col 61}{space 4} -.240698{col 74}{space 3} .6106711
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2} -.708307{col 33}{space 2} .3081877{col 44}{space 1}   -2.30{col 53}{space 3}0.022{col 61}{space 4}-1.312344{col 74}{space 3}-.1042703
{txt}career_minister_pre {c |}{col 21}{res}{space 2} .6835555{col 33}{space 2} .4876704{col 44}{space 1}    1.40{col 53}{space 3}0.161{col 61}{space 4}-.2722608{col 74}{space 3} 1.639372
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .3180467{col 33}{space 2} .3403453{col 44}{space 1}    0.93{col 53}{space 3}0.350{col 61}{space 4}-.3490179{col 74}{space 3} .9851113
{txt}{space 11}period_2 {c |}{col 21}{res}{space 2}  1.20277{col 33}{space 2}  .437188{col 44}{space 1}    2.75{col 53}{space 3}0.006{col 61}{space 4} .3458971{col 74}{space 3} 2.059643
{txt}{space 11}period_3 {c |}{col 21}{res}{space 2} 1.380046{col 33}{space 2} .4614431{col 44}{space 1}    2.99{col 53}{space 3}0.003{col 61}{space 4} .4756339{col 74}{space 3} 2.284457
{txt}{space 19} {c |}
{space 13}party2 {c |}
{space 17}2  {c |}{col 21}{res}{space 2}-2.690344{col 33}{space 2} .9512443{col 44}{space 1}   -2.83{col 53}{space 3}0.005{col 61}{space 4}-4.554749{col 74}{space 3}-.8259398
{txt}{space 17}3  {c |}{col 21}{res}{space 2}-1.817365{col 33}{space 2}  .919135{col 44}{space 1}   -1.98{col 53}{space 3}0.048{col 61}{space 4}-3.618836{col 74}{space 3}-.0158936
{txt}{space 17}5  {c |}{col 21}{res}{space 2}-.6723947{col 33}{space 2} .9492585{col 44}{space 1}   -0.71{col 53}{space 3}0.479{col 61}{space 4}-2.532907{col 74}{space 3} 1.188118
{txt}{space 17}6  {c |}{col 21}{res}{space 2}-1.182707{col 33}{space 2}  .849514{col 44}{space 1}   -1.39{col 53}{space 3}0.164{col 61}{space 4}-2.847724{col 74}{space 3} .4823093
{txt}{space 17}7  {c |}{col 21}{res}{space 2}-1.737491{col 33}{space 2} .9622682{col 44}{space 1}   -1.81{col 53}{space 3}0.071{col 61}{space 4}-3.623502{col 74}{space 3} .1485203
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-.3834764{col 33}{space 2} .8833045{col 44}{space 1}   -0.43{col 53}{space 3}0.664{col 61}{space 4}-2.114721{col 74}{space 3} 1.347769
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_rejected_full_benefits_extracted.tex"'"':regression_rejected_full_benefits_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_rejected_full_benefits_extracted.txt", label"':seeout}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1388.0011}  
Iteration 1:{space 3}log pseudolikelihood = {res: -1387.554}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1387.5526}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1387.5526}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     2,492
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      0.87
{txt}{col 49}Prob > chi2{col 67}= {res}    0.3511
{txt}Log pseudolikelihood = {res}-1387.5526{txt}{col 49}Pseudo R2{col 67}= {res}    0.0003

{txt}{ralign 82:(Std. Err. adjusted for {res:149} clusters in MP)}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}benefits_correct{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}extracted {c |}{col 18}{res}{space 2} -.255069{col 30}{space 2} .2735703{col 41}{space 1}   -0.93{col 50}{space 3}0.351{col 58}{space 4}-.7912569{col 71}{space 3}  .281119
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} -1.11641{col 30}{space 2} .1615071{col 41}{space 1}   -6.91{col 50}{space 3}0.000{col 58}{space 4}-1.432958{col 71}{space 3}-.7998623
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_rejected_full_benefits_correct_extracted.tex"'"':regression_rejected_full_benefits_correct_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_rejected_full_benefits_correct_extracted.txt", label"':seeout}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1388.0011}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1372.9945}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1372.8862}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1372.8862}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     2,492
{txt}{col 49}Wald chi2({res}4{txt}){col 67}= {res}      4.17
{txt}{col 49}Prob > chi2{col 67}= {res}    0.3835
{txt}Log pseudolikelihood = {res}-1372.8862{txt}{col 49}Pseudo R2{col 67}= {res}    0.0109

{txt}{ralign 85:(Std. Err. adjusted for {res:149} clusters in MP)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}   benefits_correct{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}extracted {c |}{col 21}{res}{space 2}-.2577957{col 33}{space 2} .2804994{col 44}{space 1}   -0.92{col 53}{space 3}0.358{col 61}{space 4}-.8075645{col 74}{space 3} .2919731
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.2504757{col 33}{space 2} .3360729{col 44}{space 1}   -0.75{col 53}{space 3}0.456{col 61}{space 4}-.9091665{col 74}{space 3} .4082151
{txt}career_minister_pre {c |}{col 21}{res}{space 2} .3952383{col 33}{space 2} .3310866{col 44}{space 1}    1.19{col 53}{space 3}0.233{col 61}{space 4}-.2536795{col 74}{space 3} 1.044156
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .5180368{col 33}{space 2} .3892458{col 44}{space 1}    1.33{col 53}{space 3}0.183{col 61}{space 4}-.2448709{col 74}{space 3} 1.280945
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.246308{col 33}{space 2} .2570299{col 44}{space 1}   -4.85{col 53}{space 3}0.000{col 61}{space 4}-1.750078{col 74}{space 3}-.7425391
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_rejected_full_benefits_correct_extracted.tex"'"':regression_rejected_full_benefits_correct_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_rejected_full_benefits_correct_extracted.txt", label"':seeout}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1388.0011}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1302.0321}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1297.5931}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1297.5712}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1297.5712}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     2,492
{txt}{col 49}Wald chi2({res}11{txt}){col 67}= {res}     17.82
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0858
{txt}Log pseudolikelihood = {res}-1297.5712{txt}{col 49}Pseudo R2{col 67}= {res}    0.0652

{txt}{ralign 85:(Std. Err. adjusted for {res:149} clusters in MP)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}   benefits_correct{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}extracted {c |}{col 21}{res}{space 2}-.2738474{col 33}{space 2}  .288258{col 44}{space 1}   -0.95{col 53}{space 3}0.342{col 61}{space 4}-.8388228{col 74}{space 3}  .291128
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.7157676{col 33}{space 2} .3660433{col 44}{space 1}   -1.96{col 53}{space 3}0.051{col 61}{space 4}-1.433199{col 74}{space 3}  .001664
{txt}career_minister_pre {c |}{col 21}{res}{space 2} .2848893{col 33}{space 2} .4427629{col 44}{space 1}    0.64{col 53}{space 3}0.520{col 61}{space 4}-.5829101{col 74}{space 3} 1.152689
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .0486422{col 33}{space 2}  .351918{col 44}{space 1}    0.14{col 53}{space 3}0.890{col 61}{space 4}-.6411043{col 74}{space 3} .7383887
{txt}{space 11}period_2 {c |}{col 21}{res}{space 2} .4115259{col 33}{space 2} .3685726{col 44}{space 1}    1.12{col 53}{space 3}0.264{col 61}{space 4}-.3108632{col 74}{space 3} 1.133915
{txt}{space 11}period_3 {c |}{col 21}{res}{space 2} .9215226{col 33}{space 2} .4022788{col 44}{space 1}    2.29{col 53}{space 3}0.022{col 61}{space 4} .1330707{col 74}{space 3} 1.709974
{txt}{space 19} {c |}
{space 13}party2 {c |}
{space 17}2  {c |}{col 21}{res}{space 2}-1.993938{col 33}{space 2} 1.014071{col 44}{space 1}   -1.97{col 53}{space 3}0.049{col 61}{space 4}-3.981481{col 74}{space 3}-.0063948
{txt}{space 17}3  {c |}{col 21}{res}{space 2}-.5596751{col 33}{space 2} .9547539{col 44}{space 1}   -0.59{col 53}{space 3}0.558{col 61}{space 4}-2.430958{col 74}{space 3} 1.311608
{txt}{space 17}5  {c |}{col 21}{res}{space 2} .5135358{col 33}{space 2} 1.174829{col 44}{space 1}    0.44{col 53}{space 3}0.662{col 61}{space 4}-1.789086{col 74}{space 3} 2.816158
{txt}{space 17}6  {c |}{col 21}{res}{space 2}-.1449324{col 33}{space 2} .9317287{col 44}{space 1}   -0.16{col 53}{space 3}0.876{col 61}{space 4}-1.971087{col 74}{space 3} 1.681222
{txt}{space 17}7  {c |}{col 21}{res}{space 2}-.9808353{col 33}{space 2} 1.122039{col 44}{space 1}   -0.87{col 53}{space 3}0.382{col 61}{space 4}-3.179991{col 74}{space 3}  1.21832
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-.7843737{col 33}{space 2} .9037348{col 44}{space 1}   -0.87{col 53}{space 3}0.385{col 61}{space 4}-2.555661{col 74}{space 3}  .986914
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_rejected_full_benefits_correct_extracted.tex"'"':regression_rejected_full_benefits_correct_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_rejected_full_benefits_correct_extracted.txt", label"':seeout}

{com}. 
. *Appendix (Table A18-A19)
. foreach y in benefits benefits_correct  {c -(}
{txt}  2{com}. foreach x in extracted {c -(}
{txt}  3{com}. firthlogit `y' `x' 
{txt}  4{com}. outreg2 using regression_firthlogit_rejected_full_`y'_`x' , tex replace keep(`x') label title("Private Benefits")  addtext()
{txt}  5{com}. firthlogit `y' `x' list_constnum career_minister_pre committee_chair
{txt}  6{com}. outreg2 using regression_firthlogit_rejected_full_`y'_`x', tex append  keep(`x' list_constnum career_minister_pre committee_chair) label title("Private Benefits") addtext()
{txt}  7{com}. firthlogit `y' `x' list_constnum career_minister_pre committee_chair period_2 period_3 i.party2
{txt}  8{com}. outreg2 using regression_firthlogit_rejected_full_`y'_`x', tex append  keep( `x' list_constnum career_minister_pre committee_chair) label title("Private Benefits") addtext(Party FE, YES, Legislative Period FE, YES)
{txt}  9{com}. {c )-}
{txt} 10{com}. {c )-}
{res}
{txt}initial:{col 16}penalized log likelihood = {res:-1804.2589}
rescale:{col 16}penalized log likelihood = {res:-1804.2589}
{res}{txt}Iteration 0:{space 3}penalized log likelihood = {res:-1804.2589}  
Iteration 1:{space 3}penalized log likelihood = {res:-1804.0799}  
Iteration 2:{space 3}penalized log likelihood = {res:-1804.0798}  
{res}
{txt}{col 49}Number of obs{col 67}= {res}     3,040
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      0.36
{txt}Penalized log likelihood = {res}-1804.0798{txt}{col 49}Prob > chi2{col 67}= {res}    0.5465

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    benefits{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}extracted {c |}{col 14}{res}{space 2} .1315934{col 26}{space 2} .2182023{col 37}{space 1}    0.60{col 46}{space 3}0.546{col 54}{space 4}-.2960753{col 67}{space 3} .5592621
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.9372183{col 26}{space 2} .0410101{col 37}{space 1}  -22.85{col 46}{space 3}0.000{col 54}{space 4}-1.017597{col 67}{space 3}-.8568398
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_firthlogit_rejected_full_benefits_extracted.tex"'"':regression_firthlogit_rejected_full_benefits_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_firthlogit_rejected_full_benefits_extracted.txt", label"':seeout}
{res}
{txt}initial:{col 16}penalized log likelihood = {res: -1797.231}
rescale:{col 16}penalized log likelihood = {res: -1797.231}
{res}{txt}Iteration 0:{space 3}penalized log likelihood = {res: -1797.231}  
Iteration 1:{space 3}penalized log likelihood = {res:-1760.6379}  
Iteration 2:{space 3}penalized log likelihood = {res:-1760.3135}  
Iteration 3:{space 3}penalized log likelihood = {res:-1760.3134}  
{res}
{txt}{col 49}Number of obs{col 67}= {res}     3,040
{txt}{col 49}Wald chi2({res}4{txt}){col 67}= {res}     73.33
{txt}Penalized log likelihood = {res}-1760.3134{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           benefits{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}extracted {c |}{col 21}{res}{space 2} .1355588{col 33}{space 2} .2211722{col 44}{space 1}    0.61{col 53}{space 3}0.540{col 61}{space 4}-.2979307{col 74}{space 3} .5690483
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.3312136{col 33}{space 2} .0882016{col 44}{space 1}   -3.76{col 53}{space 3}0.000{col 61}{space 4}-.5040855{col 74}{space 3}-.1583416
{txt}career_minister_pre {c |}{col 21}{res}{space 2} .4871855{col 33}{space 2} .0865847{col 44}{space 1}    5.63{col 53}{space 3}0.000{col 61}{space 4} .3174827{col 74}{space 3} .6568884
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2}  .775652{col 33}{space 2} .1195817{col 44}{space 1}    6.49{col 53}{space 3}0.000{col 61}{space 4} .5412762{col 74}{space 3} 1.010028
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.091336{col 33}{space 2} .0639097{col 44}{space 1}  -17.08{col 53}{space 3}0.000{col 61}{space 4}-1.216597{col 74}{space 3}-.9660752
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_firthlogit_rejected_full_benefits_extracted.tex"'"':regression_firthlogit_rejected_full_benefits_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_firthlogit_rejected_full_benefits_extracted.txt", label"':seeout}
{res}
{txt}initial:{col 16}penalized log likelihood = {res:-1784.4589}
rescale:{col 16}penalized log likelihood = {res:-1784.4589}
{res}{txt}Iteration 0:{space 3}penalized log likelihood = {res:-1784.4589}  
Iteration 1:{space 3}penalized log likelihood = {res:-1656.6793}  
Iteration 2:{space 3}penalized log likelihood = {res:-1651.2133}  
Iteration 3:{space 3}penalized log likelihood = {res:-1651.1842}  
Iteration 4:{space 3}penalized log likelihood = {res:-1651.1842}  
{res}
{txt}{col 49}Number of obs{col 67}= {res}     3,040
{txt}{col 49}Wald chi2({res}11{txt}){col 67}= {res}    219.29
{txt}Penalized log likelihood = {res}-1651.1842{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           benefits{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}extracted {c |}{col 21}{res}{space 2} .1919039{col 33}{space 2} .2297048{col 44}{space 1}    0.84{col 53}{space 3}0.403{col 61}{space 4}-.2583093{col 74}{space 3} .6421172
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.7046861{col 33}{space 2} .1021907{col 44}{space 1}   -6.90{col 53}{space 3}0.000{col 61}{space 4}-.9049762{col 74}{space 3}-.5043959
{txt}career_minister_pre {c |}{col 21}{res}{space 2} .6787938{col 33}{space 2} .1492137{col 44}{space 1}    4.55{col 53}{space 3}0.000{col 61}{space 4} .3863404{col 74}{space 3} .9712472
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .3178803{col 33}{space 2} .1442034{col 44}{space 1}    2.20{col 53}{space 3}0.027{col 61}{space 4} .0352469{col 74}{space 3} .6005137
{txt}{space 11}period_2 {c |}{col 21}{res}{space 2} 1.189608{col 33}{space 2}  .169369{col 44}{space 1}    7.02{col 53}{space 3}0.000{col 61}{space 4} .8576511{col 74}{space 3} 1.521565
{txt}{space 11}period_3 {c |}{col 21}{res}{space 2} 1.365554{col 33}{space 2}  .170325{col 44}{space 1}    8.02{col 53}{space 3}0.000{col 61}{space 4} 1.031723{col 74}{space 3} 1.699385
{txt}{space 19} {c |}
{space 13}party2 {c |}
{space 17}2  {c |}{col 21}{res}{space 2}-2.677399{col 33}{space 2} .3138747{col 44}{space 1}   -8.53{col 53}{space 3}0.000{col 61}{space 4}-3.292582{col 74}{space 3}-2.062216
{txt}{space 17}3  {c |}{col 21}{res}{space 2}-1.807505{col 33}{space 2} .3103952{col 44}{space 1}   -5.82{col 53}{space 3}0.000{col 61}{space 4}-2.415868{col 74}{space 3}-1.199141
{txt}{space 17}5  {c |}{col 21}{res}{space 2}-.6609328{col 33}{space 2}   .39262{col 44}{space 1}   -1.68{col 53}{space 3}0.092{col 61}{space 4}-1.430454{col 74}{space 3} .1085882
{txt}{space 17}6  {c |}{col 21}{res}{space 2}-1.176074{col 33}{space 2} .2951504{col 44}{space 1}   -3.98{col 53}{space 3}0.000{col 61}{space 4}-1.754558{col 74}{space 3}-.5975902
{txt}{space 17}7  {c |}{col 21}{res}{space 2}-1.727597{col 33}{space 2} .3168872{col 44}{space 1}   -5.45{col 53}{space 3}0.000{col 61}{space 4}-2.348684{col 74}{space 3}-1.106509
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-.3760746{col 33}{space 2} .2758601{col 44}{space 1}   -1.36{col 53}{space 3}0.173{col 61}{space 4}-.9167506{col 74}{space 3} .1646013
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_firthlogit_rejected_full_benefits_extracted.tex"'"':regression_firthlogit_rejected_full_benefits_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_firthlogit_rejected_full_benefits_extracted.txt", label"':seeout}
{res}
{txt}initial:{col 16}penalized log likelihood = {res:-1383.5794}
rescale:{col 16}penalized log likelihood = {res:-1383.5794}
{res}{txt}Iteration 0:{space 3}penalized log likelihood = {res:-1383.5794}  
Iteration 1:{space 3}penalized log likelihood = {res:-1383.1945}  
Iteration 2:{space 3}penalized log likelihood = {res: -1383.194}  
Iteration 3:{space 3}penalized log likelihood = {res: -1383.194}  
{res}
{txt}{col 49}Number of obs{col 67}= {res}     2,492
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      0.74
{txt}Penalized log likelihood = {res} -1383.194{txt}{col 49}Prob > chi2{col 67}= {res}    0.3901

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}benefits_c~t{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}extracted {c |}{col 14}{res}{space 2}-.2340824{col 26}{space 2}   .27238{col 37}{space 1}   -0.86{col 46}{space 3}0.390{col 54}{space 4}-.7679375{col 67}{space 3} .2997726
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-1.115845{col 26}{space 2} .0472568{col 37}{space 1}  -23.61{col 46}{space 3}0.000{col 54}{space 4}-1.208466{col 67}{space 3}-1.023223
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_firthlogit_rejected_full_benefits_correct_extracted.tex"'"':regression_firthlogit_rejected_full_benefits_correct_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_firthlogit_rejected_full_benefits_correct_extracted.txt", label"':seeout}
{res}
{txt}initial:{col 16}penalized log likelihood = {res:-1376.9956}
rescale:{col 16}penalized log likelihood = {res:-1376.9956}
{res}{txt}Iteration 0:{space 3}penalized log likelihood = {res:-1376.9956}  
Iteration 1:{space 3}penalized log likelihood = {res:-1362.0027}  
Iteration 2:{space 3}penalized log likelihood = {res:-1361.8958}  
Iteration 3:{space 3}penalized log likelihood = {res:-1361.8958}  
{res}
{txt}{col 49}Number of obs{col 67}= {res}     2,492
{txt}{col 49}Wald chi2({res}4{txt}){col 67}= {res}     30.35
{txt}Penalized log likelihood = {res}-1361.8958{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   benefits_correct{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}extracted {c |}{col 21}{res}{space 2}-.2367485{col 33}{space 2} .2740945{col 44}{space 1}   -0.86{col 53}{space 3}0.388{col 61}{space 4}-.7739639{col 74}{space 3} .3004668
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.2495789{col 33}{space 2} .1015214{col 44}{space 1}   -2.46{col 53}{space 3}0.014{col 61}{space 4}-.4485572{col 74}{space 3}-.0506005
{txt}career_minister_pre {c |}{col 21}{res}{space 2} .3943451{col 33}{space 2} .0992532{col 44}{space 1}    3.97{col 53}{space 3}0.000{col 61}{space 4} .1998123{col 74}{space 3} .5888778
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .5191681{col 33}{space 2} .1395101{col 44}{space 1}    3.72{col 53}{space 3}0.000{col 61}{space 4} .2457333{col 74}{space 3}  .792603
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.244428{col 33}{space 2} .0736812{col 44}{space 1}  -16.89{col 53}{space 3}0.000{col 61}{space 4} -1.38884{col 74}{space 3}-1.100015
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_firthlogit_rejected_full_benefits_correct_extracted.tex"'"':regression_firthlogit_rejected_full_benefits_correct_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_firthlogit_rejected_full_benefits_correct_extracted.txt", label"':seeout}
{res}
{txt}initial:{col 16}penalized log likelihood = {res:-1365.1484}
rescale:{col 16}penalized log likelihood = {res:-1365.1484}
{res}{txt}Iteration 0:{space 3}penalized log likelihood = {res:-1365.1484}  
Iteration 1:{space 3}penalized log likelihood = {res:-1279.5196}  
Iteration 2:{space 3}penalized log likelihood = {res:-1275.1028}  
Iteration 3:{space 3}penalized log likelihood = {res:-1275.0935}  
Iteration 4:{space 3}penalized log likelihood = {res:-1275.0935}  
{res}
{txt}{col 49}Number of obs{col 67}= {res}     2,492
{txt}{col 49}Wald chi2({res}11{txt}){col 67}= {res}    149.62
{txt}Penalized log likelihood = {res}-1275.0935{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   benefits_correct{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}extracted {c |}{col 21}{res}{space 2}-.2527564{col 33}{space 2} .2827411{col 44}{space 1}   -0.89{col 53}{space 3}0.371{col 61}{space 4}-.8069188{col 74}{space 3} .3014061
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.7111567{col 33}{space 2}  .114114{col 44}{space 1}   -6.23{col 53}{space 3}0.000{col 61}{space 4}-.9348161{col 74}{space 3}-.4874974
{txt}career_minister_pre {c |}{col 21}{res}{space 2} .2823641{col 33}{space 2} .1623464{col 44}{space 1}    1.74{col 53}{space 3}0.082{col 61}{space 4} -.035829{col 74}{space 3} .6005572
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .0504035{col 33}{space 2}  .167353{col 44}{space 1}    0.30{col 53}{space 3}0.763{col 61}{space 4}-.2776024{col 74}{space 3} .3784094
{txt}{space 11}period_2 {c |}{col 21}{res}{space 2} .4046708{col 33}{space 2} .1575818{col 44}{space 1}    2.57{col 53}{space 3}0.010{col 61}{space 4} .0958161{col 74}{space 3} .7135255
{txt}{space 11}period_3 {c |}{col 21}{res}{space 2} .9117327{col 33}{space 2} .1643968{col 44}{space 1}    5.55{col 53}{space 3}0.000{col 61}{space 4} .5895208{col 74}{space 3} 1.233945
{txt}{space 19} {c |}
{space 13}party2 {c |}
{space 17}2  {c |}{col 21}{res}{space 2}-1.997679{col 33}{space 2} .3574907{col 44}{space 1}   -5.59{col 53}{space 3}0.000{col 61}{space 4}-2.698348{col 74}{space 3} -1.29701
{txt}{space 17}3  {c |}{col 21}{res}{space 2}-.5724551{col 33}{space 2} .3466077{col 44}{space 1}   -1.65{col 53}{space 3}0.099{col 61}{space 4}-1.251794{col 74}{space 3} .1068834
{txt}{space 17}5  {c |}{col 21}{res}{space 2} .5013377{col 33}{space 2} .4177635{col 44}{space 1}    1.20{col 53}{space 3}0.230{col 61}{space 4}-.3174636{col 74}{space 3} 1.320139
{txt}{space 17}6  {c |}{col 21}{res}{space 2}-.1589463{col 33}{space 2} .3335108{col 44}{space 1}   -0.48{col 53}{space 3}0.634{col 61}{space 4}-.8126155{col 74}{space 3} .4947228
{txt}{space 17}7  {c |}{col 21}{res}{space 2}-.9886097{col 33}{space 2} .3628271{col 44}{space 1}   -2.72{col 53}{space 3}0.006{col 61}{space 4}-1.699738{col 74}{space 3}-.2774817
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-.7617148{col 33}{space 2} .3113886{col 44}{space 1}   -2.45{col 53}{space 3}0.014{col 61}{space 4}-1.372025{col 74}{space 3}-.1514044
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_firthlogit_rejected_full_benefits_correct_extracted.tex"'"':regression_firthlogit_rejected_full_benefits_correct_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_firthlogit_rejected_full_benefits_correct_extracted.txt", label"':seeout}

{com}. 
. 
. ***************************************************************************
. **Analysis Benefits - MP Level*********************************************
. ***************************************************************************
. 
. use datasetNZballot_Aug2017_prepared, clear
{txt}(Written by R.              )

{com}. 
. keep extracted bill_passed benefits benefits_correct benefits_ord list_constnum committee_chair  career_minister_pre  period_2 period_3 government  party2 party3 ballotyear bill
{txt}
{com}. 
. collapse (mean) extracted bill_passe benefits benefits_correct benefits_ord list_constnum   committee_chair career_minister_pre  period_2 period_3 government party2, by(bill ballotyear)
{txt}
{com}. 
. gen extracted_col=0
{txt}
{com}. replace extracted_col=1 if extracted>0
{txt}(117 real changes made)

{com}. 
. gen benefits_col=0
{txt}
{com}. replace benefits_col=1 if benefits>0
{txt}(198 real changes made)

{com}. 
. gen committee_chair_col=0
{txt}
{com}. replace committee_chair_col=1 if committee_chair>0
{txt}(73 real changes made)

{com}. 
. tostring party2, generate(party2_st) force
{txt}party2_st generated as {res:str11}
party2_st was forced to string; some loss of information

{com}. 
. encode party2_st, gen(party2_st1)
{txt}
{com}. 
. *Appendix (Table A20-A21)
. foreach y in benefits_col {c -(}
{txt}  2{com}. foreach x in extracted_col bill_passed {c -(}
{txt}  3{com}. logit `y' `x', cluster(bill)
{txt}  4{com}. outreg2 using regression_coll_`y'_`x' , tex replace keep(`x') label title("Private Benefits - Collapsed") addtext(Clustered SE, YES)
{txt}  5{com}. logit `y' `x' list_constnum career_minister_pre committee_chair_col, cluster(bill)
{txt}  6{com}. outreg2 using regression_coll_`y'_`x', tex append keep(`x' list_constnum career_minister_pre committee_chair_col) label title("Private Benefits - Collapsed")  addtext(Clustered SE, YES)
{txt}  7{com}. logit `y' `x' list_constnum career_minister_pre committee_chair_col period_2 period_3 government, cluster(bill)
{txt}  8{com}. outreg2 using regression_coll_`y'_`x', tex append keep( `x' list_constnum career_minister_pre committee_chair_col) label title("Private Benefits - Collapsed")  addtext(Clustered SE, YES, Party FE, YES)
{txt}  9{com}. {c )-}
{txt} 10{com}. {c )-}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-409.83272}  
Iteration 1:{space 3}log pseudolikelihood = {res:-407.93526}  
Iteration 2:{space 3}log pseudolikelihood = {res:-407.92811}  
Iteration 3:{space 3}log pseudolikelihood = {res:-407.92811}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       679
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      3.93
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0474
{txt}Log pseudolikelihood = {res}-407.92811{txt}{col 49}Pseudo R2{col 67}= {res}    0.0046

{txt}{ralign 79:(Std. Err. adjusted for {res:362} clusters in bill)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1} benefits_col{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
extracted_col {c |}{col 15}{res}{space 2} .4225231{col 27}{space 2} .2130958{col 38}{space 1}    1.98{col 47}{space 3}0.047{col 55}{space 4}  .004863{col 68}{space 3} .8401832
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}-.9653881{col 27}{space 2} .1089994{col 38}{space 1}   -8.86{col 47}{space 3}0.000{col 55}{space 4}-1.179023{col 68}{space 3}-.7517531
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_coll_benefits_col_extracted_col.tex"'"':regression_coll_benefits_col_extracted_col.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_coll_benefits_col_extracted_col.txt", label"':seeout}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-409.83272}  
Iteration 1:{space 3}log pseudolikelihood = {res:  -403.738}  
Iteration 2:{space 3}log pseudolikelihood = {res:-403.69409}  
Iteration 3:{space 3}log pseudolikelihood = {res:-403.69409}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       679
{txt}{col 49}Wald chi2({res}4{txt}){col 67}= {res}     12.95
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0115
{txt}Log pseudolikelihood = {res}-403.69409{txt}{col 49}Pseudo R2{col 67}= {res}    0.0150

{txt}{ralign 85:(Std. Err. adjusted for {res:362} clusters in bill)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}       benefits_col{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}extracted_col {c |}{col 21}{res}{space 2} .4130037{col 33}{space 2}   .21553{col 44}{space 1}    1.92{col 53}{space 3}0.055{col 61}{space 4}-.0094273{col 74}{space 3} .8354346
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2} .0337848{col 33}{space 2} .2171518{col 44}{space 1}    0.16{col 53}{space 3}0.876{col 61}{space 4} -.391825{col 74}{space 3} .4593946
{txt}career_minister_pre {c |}{col 21}{res}{space 2} .2266365{col 33}{space 2}  .208136{col 44}{space 1}    1.09{col 53}{space 3}0.276{col 61}{space 4}-.1813025{col 74}{space 3} .6345756
{txt}committee_chair_col {c |}{col 21}{res}{space 2}  .621374{col 33}{space 2} .2543439{col 44}{space 1}    2.44{col 53}{space 3}0.015{col 61}{space 4} .1228692{col 74}{space 3} 1.119879
{txt}{space 14}_cons {c |}{col 21}{res}{space 2} -1.15472{col 33}{space 2} .1626843{col 44}{space 1}   -7.10{col 53}{space 3}0.000{col 61}{space 4}-1.473575{col 74}{space 3}-.8358644
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_coll_benefits_col_extracted_col.tex"'"':regression_coll_benefits_col_extracted_col.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_coll_benefits_col_extracted_col.txt", label"':seeout}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-409.83272}  
Iteration 1:{space 3}log pseudolikelihood = {res:-385.18463}  
Iteration 2:{space 3}log pseudolikelihood = {res:-384.51295}  
Iteration 3:{space 3}log pseudolikelihood = {res:-384.51083}  
Iteration 4:{space 3}log pseudolikelihood = {res:-384.51083}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       679
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     36.14
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-384.51083{txt}{col 49}Pseudo R2{col 67}= {res}    0.0618

{txt}{ralign 85:(Std. Err. adjusted for {res:362} clusters in bill)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}       benefits_col{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}extracted_col {c |}{col 21}{res}{space 2}  .406084{col 33}{space 2}  .221074{col 44}{space 1}    1.84{col 53}{space 3}0.066{col 61}{space 4} -.027213{col 74}{space 3}  .839381
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.2207769{col 33}{space 2} .2185772{col 44}{space 1}   -1.01{col 53}{space 3}0.312{col 61}{space 4}-.6491803{col 74}{space 3} .2076265
{txt}career_minister_pre {c |}{col 21}{res}{space 2}  .664374{col 33}{space 2} .2423075{col 44}{space 1}    2.74{col 53}{space 3}0.006{col 61}{space 4}   .18946{col 74}{space 3} 1.139288
{txt}committee_chair_col {c |}{col 21}{res}{space 2} .0423651{col 33}{space 2} .2757685{col 44}{space 1}    0.15{col 53}{space 3}0.878{col 61}{space 4}-.4981312{col 74}{space 3} .5828614
{txt}{space 11}period_2 {c |}{col 21}{res}{space 2} 1.040028{col 33}{space 2} .3113867{col 44}{space 1}    3.34{col 53}{space 3}0.001{col 61}{space 4} .4297209{col 74}{space 3} 1.650334
{txt}{space 11}period_3 {c |}{col 21}{res}{space 2} 1.475291{col 33}{space 2} .3339958{col 44}{space 1}    4.42{col 53}{space 3}0.000{col 61}{space 4} .8206718{col 74}{space 3} 2.129911
{txt}{space 9}government {c |}{col 21}{res}{space 2} .9388914{col 33}{space 2} .2594025{col 44}{space 1}    3.62{col 53}{space 3}0.000{col 61}{space 4} .4304719{col 74}{space 3} 1.447311
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-2.489583{col 33}{space 2} .3459834{col 44}{space 1}   -7.20{col 53}{space 3}0.000{col 61}{space 4}-3.167698{col 74}{space 3}-1.811469
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_coll_benefits_col_extracted_col.tex"'"':regression_coll_benefits_col_extracted_col.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_coll_benefits_col_extracted_col.txt", label"':seeout}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-76.941928}  
Iteration 1:{space 3}log pseudolikelihood = {res:-74.406592}  
Iteration 2:{space 3}log pseudolikelihood = {res:-74.400911}  
Iteration 3:{space 3}log pseudolikelihood = {res:-74.400911}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       117
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      4.83
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0280
{txt}Log pseudolikelihood = {res}-74.400911{txt}{col 49}Pseudo R2{col 67}= {res}    0.0330

{txt}{ralign 78:(Std. Err. adjusted for {res:115} clusters in bill)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}benefits_col{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}bill_passed {c |}{col 14}{res}{space 2} 1.233826{col 26}{space 2}    .5615{col 37}{space 1}    2.20{col 46}{space 3}0.028{col 54}{space 4}  .133306{col 67}{space 3} 2.334345
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.7230001{col 26}{space 2} .2151194{col 37}{space 1}   -3.36{col 46}{space 3}0.001{col 54}{space 4}-1.144626{col 67}{space 3}-.3013738
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_coll_benefits_col_bill_passed.tex"'"':regression_coll_benefits_col_bill_passed.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_coll_benefits_col_bill_passed.txt", label"':seeout}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-76.941928}  
Iteration 1:{space 3}log pseudolikelihood = {res:-73.072353}  
Iteration 2:{space 3}log pseudolikelihood = {res:-73.068426}  
Iteration 3:{space 3}log pseudolikelihood = {res:-73.068426}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       117
{txt}{col 49}Wald chi2({res}4{txt}){col 67}= {res}      6.50
{txt}{col 49}Prob > chi2{col 67}= {res}    0.1649
{txt}Log pseudolikelihood = {res}-73.068426{txt}{col 49}Pseudo R2{col 67}= {res}    0.0503

{txt}{ralign 85:(Std. Err. adjusted for {res:115} clusters in bill)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}       benefits_col{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}bill_passed {c |}{col 21}{res}{space 2} 1.110495{col 33}{space 2} .5848104{col 44}{space 1}    1.90{col 53}{space 3}0.058{col 61}{space 4}-.0357125{col 74}{space 3} 2.256702
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2} .0948445{col 33}{space 2} .4504328{col 44}{space 1}    0.21{col 53}{space 3}0.833{col 61}{space 4}-.7879876{col 74}{space 3} .9776765
{txt}career_minister_pre {c |}{col 21}{res}{space 2}-.1481994{col 33}{space 2} .4489731{col 44}{space 1}   -0.33{col 53}{space 3}0.741{col 61}{space 4}-1.028171{col 74}{space 3} .7317718
{txt}committee_chair_col {c |}{col 21}{res}{space 2} .9974309{col 33}{space 2}  .648851{col 44}{space 1}    1.54{col 53}{space 3}0.124{col 61}{space 4}-.2742936{col 74}{space 3} 2.269155
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-.7999766{col 33}{space 2} .3171381{col 44}{space 1}   -2.52{col 53}{space 3}0.012{col 61}{space 4}-1.421556{col 74}{space 3}-.1783973
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_coll_benefits_col_bill_passed.tex"'"':regression_coll_benefits_col_bill_passed.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_coll_benefits_col_bill_passed.txt", label"':seeout}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-76.941928}  
Iteration 1:{space 3}log pseudolikelihood = {res:-65.637768}  
Iteration 2:{space 3}log pseudolikelihood = {res:-65.449314}  
Iteration 3:{space 3}log pseudolikelihood = {res:-65.448592}  
Iteration 4:{space 3}log pseudolikelihood = {res:-65.448592}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       117
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     21.14
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0036
{txt}Log pseudolikelihood = {res}-65.448592{txt}{col 49}Pseudo R2{col 67}= {res}    0.1494

{txt}{ralign 85:(Std. Err. adjusted for {res:115} clusters in bill)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}       benefits_col{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}bill_passed {c |}{col 21}{res}{space 2} .9909726{col 33}{space 2} .6094181{col 44}{space 1}    1.63{col 53}{space 3}0.104{col 61}{space 4} -.203465{col 74}{space 3}  2.18541
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.1384986{col 33}{space 2} .4720078{col 44}{space 1}   -0.29{col 53}{space 3}0.769{col 61}{space 4}-1.063617{col 74}{space 3} .7866196
{txt}career_minister_pre {c |}{col 21}{res}{space 2} .5527391{col 33}{space 2} .5537713{col 44}{space 1}    1.00{col 53}{space 3}0.318{col 61}{space 4}-.5326326{col 74}{space 3} 1.638111
{txt}committee_chair_col {c |}{col 21}{res}{space 2} .0111455{col 33}{space 2} .6658775{col 44}{space 1}    0.02{col 53}{space 3}0.987{col 61}{space 4} -1.29395{col 74}{space 3} 1.316241
{txt}{space 11}period_2 {c |}{col 21}{res}{space 2} 1.177532{col 33}{space 2} .6994208{col 44}{space 1}    1.68{col 53}{space 3}0.092{col 61}{space 4}-.1933078{col 74}{space 3} 2.548371
{txt}{space 11}period_3 {c |}{col 21}{res}{space 2} 1.990936{col 33}{space 2} .6965433{col 44}{space 1}    2.86{col 53}{space 3}0.004{col 61}{space 4} .6257359{col 74}{space 3} 3.356135
{txt}{space 9}government {c |}{col 21}{res}{space 2} 1.595238{col 33}{space 2} .6065015{col 44}{space 1}    2.63{col 53}{space 3}0.009{col 61}{space 4} .4065167{col 74}{space 3} 2.783959
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-2.714728{col 33}{space 2} .7409339{col 44}{space 1}   -3.66{col 53}{space 3}0.000{col 61}{space 4}-4.166932{col 74}{space 3}-1.262525
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_coll_benefits_col_bill_passed.tex"'"':regression_coll_benefits_col_bill_passed.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_coll_benefits_col_bill_passed.txt", label"':seeout}

{com}. 
. save datasetNZballot_Aug2017_collapsed, replace
{txt}file datasetNZballot_Aug2017_collapsed.dta saved

{com}. 
. ***************************************************************************
. **Analysis Benefits - Comparison Passed v. Rejected Bills - MP Level*******
. ***************************************************************************
. 
. *Appendix (Table A22)
. drop if bill_passed==0
{txt}(101 observations deleted)

{com}. 
. foreach y in benefits {c -(}
{txt}  2{com}. foreach x in extracted {c -(}
{txt}  3{com}. logit `y' `x', cluster(bill)
{txt}  4{com}. outreg2 using regression_coll_comp_`y'_`x', tex replace keep(`x') label title("Private Benefits - Collapsed - Bill Passed") addtext(Robust SE, YES)
{txt}  5{com}. logit `y' `x' list_constnum career_minister_pre committee_chair_col, cluster(bill)
{txt}  6{com}. outreg2 using regression_coll_comp_`y'_`x', tex append keep(`x' list_constnum career_minister_pre committee_chair_col) label title("Private Benefits - Collapsed - Bill Passed") addtext(Robust SE, YES)
{txt}  7{com}. logit `y' `x' list_constnum career_minister_pre committee_chair_col i.party2_st1, cluster(bill)
{txt}  8{com}. outreg2 using regression_coll_comp_`y'_`x', tex append keep( `x' list_constnum career_minister_pre committee_chair_col) label title("Private Benefits - Collapsed - Bill Passed") addtext(Robust SE, YES, Party FE, YES)
{txt}  9{com}. {c )-}
{txt} 10{com}. {c )-}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-345.66884}  
Iteration 1:{space 3}log pseudolikelihood = {res:-343.82913}  
Iteration 2:{space 3}log pseudolikelihood = {res:-343.79761}  
Iteration 3:{space 3}log pseudolikelihood = {res:-343.79756}  
Iteration 4:{space 3}log pseudolikelihood = {res:-343.79756}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       578
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      2.26
{txt}{col 49}Prob > chi2{col 67}= {res}    0.1327
{txt}Log pseudolikelihood = {res}-343.79756{txt}{col 49}Pseudo R2{col 67}= {res}    0.0054

{txt}{ralign 78:(Std. Err. adjusted for {res:310} clusters in bill)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    benefits{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}extracted {c |}{col 14}{res}{space 2} 1.871304{col 26}{space 2} 1.244726{col 37}{space 1}    1.50{col 46}{space 3}0.133{col 54}{space 4}-.5683135{col 67}{space 3} 4.310921
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} -.944903{col 26}{space 2} .1077133{col 37}{space 1}   -8.77{col 46}{space 3}0.000{col 54}{space 4}-1.156017{col 67}{space 3}-.7337889
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_coll_comp_benefits_extracted.tex"'"':regression_coll_comp_benefits_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_coll_comp_benefits_extracted.txt", label"':seeout}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-345.66884}  
Iteration 1:{space 3}log pseudolikelihood = {res:-339.86776}  
Iteration 2:{space 3}log pseudolikelihood = {res:-339.81767}  
Iteration 3:{space 3}log pseudolikelihood = {res:-339.81764}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       578
{txt}{col 49}Wald chi2({res}4{txt}){col 67}= {res}     11.93
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0179
{txt}Log pseudolikelihood = {res}-339.81764{txt}{col 49}Pseudo R2{col 67}= {res}    0.0169

{txt}{ralign 85:(Std. Err. adjusted for {res:310} clusters in bill)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}           benefits{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}extracted {c |}{col 21}{res}{space 2} 1.747109{col 33}{space 2} 1.149614{col 44}{space 1}    1.52{col 53}{space 3}0.129{col 61}{space 4} -.506093{col 74}{space 3} 4.000311
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2} .0008207{col 33}{space 2} .2367346{col 44}{space 1}    0.00{col 53}{space 3}0.997{col 61}{space 4}-.4631706{col 74}{space 3} .4648121
{txt}career_minister_pre {c |}{col 21}{res}{space 2}  .363145{col 33}{space 2} .2225599{col 44}{space 1}    1.63{col 53}{space 3}0.103{col 61}{space 4}-.0730644{col 74}{space 3} .7993544
{txt}committee_chair_col {c |}{col 21}{res}{space 2} .5318066{col 33}{space 2} .2761556{col 44}{space 1}    1.93{col 53}{space 3}0.054{col 61}{space 4}-.0094485{col 74}{space 3} 1.073062
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.168651{col 33}{space 2} .1706111{col 44}{space 1}   -6.85{col 53}{space 3}0.000{col 61}{space 4}-1.503042{col 74}{space 3}-.8342594
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_coll_comp_benefits_extracted.tex"'"':regression_coll_comp_benefits_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_coll_comp_benefits_extracted.txt", label"':seeout}

note: 4.party2_st1 != 0 predicts success perfectly
      4.party2_st1 dropped and 1 obs not used

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-344.41304}  
Iteration 1:{space 3}log pseudolikelihood = {res:-331.96303}  
Iteration 2:{space 3}log pseudolikelihood = {res:-331.60173}  
Iteration 3:{space 3}log pseudolikelihood = {res:-331.60043}  
Iteration 4:{space 3}log pseudolikelihood = {res:-331.60043}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       577
{txt}{col 49}Wald chi2({res}9{txt}){col 67}= {res}     19.44
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0217
{txt}Log pseudolikelihood = {res}-331.60043{txt}{col 49}Pseudo R2{col 67}= {res}    0.0372

{txt}{ralign 85:(Std. Err. adjusted for {res:310} clusters in bill)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}           benefits{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}extracted {c |}{col 21}{res}{space 2}  1.35117{col 33}{space 2} 1.173774{col 44}{space 1}    1.15{col 53}{space 3}0.250{col 61}{space 4}-.9493849{col 74}{space 3} 3.651725
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.2712665{col 33}{space 2} .2532553{col 44}{space 1}   -1.07{col 53}{space 3}0.284{col 61}{space 4}-.7676377{col 74}{space 3} .2251048
{txt}career_minister_pre {c |}{col 21}{res}{space 2} .0695282{col 33}{space 2} .3353493{col 44}{space 1}    0.21{col 53}{space 3}0.836{col 61}{space 4}-.5877443{col 74}{space 3} .7268007
{txt}committee_chair_col {c |}{col 21}{res}{space 2} .3797737{col 33}{space 2} .3085834{col 44}{space 1}    1.23{col 53}{space 3}0.218{col 61}{space 4}-.2250386{col 74}{space 3}  .984586
{txt}{space 19} {c |}
{space 9}party2_st1 {c |}
{space 17}2  {c |}{col 21}{res}{space 2}-1.274411{col 33}{space 2}  .657283{col 44}{space 1}   -1.94{col 53}{space 3}0.053{col 61}{space 4}-2.562662{col 74}{space 3} .0138404
{txt}{space 17}3  {c |}{col 21}{res}{space 2}-.1444839{col 33}{space 2}  .649076{col 44}{space 1}   -0.22{col 53}{space 3}0.824{col 61}{space 4}-1.416649{col 74}{space 3} 1.127682
{txt}{space 7}3.285714388  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 17}5  {c |}{col 21}{res}{space 2}-.0631544{col 33}{space 2} .8002108{col 44}{space 1}   -0.08{col 53}{space 3}0.937{col 61}{space 4}-1.631539{col 74}{space 3}  1.50523
{txt}{space 17}6  {c |}{col 21}{res}{space 2}-.0608939{col 33}{space 2} .6388123{col 44}{space 1}   -0.10{col 53}{space 3}0.924{col 61}{space 4}-1.312943{col 74}{space 3} 1.191155
{txt}{space 17}7  {c |}{col 21}{res}{space 2}-.3618521{col 33}{space 2} .6978912{col 44}{space 1}   -0.52{col 53}{space 3}0.604{col 61}{space 4}-1.729694{col 74}{space 3}  1.00599
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-.5566934{col 33}{space 2} .5971691{col 44}{space 1}   -0.93{col 53}{space 3}0.351{col 61}{space 4}-1.727123{col 74}{space 3} .6137365
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_coll_comp_benefits_extracted.tex"'"':regression_coll_comp_benefits_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_coll_comp_benefits_extracted.txt", label"':seeout}

{com}. 
. use datasetNZballot_Aug2017_collapsed, clear
{txt}(Written by R.              )

{com}. 
. drop if bill_passed==1
{txt}(16 observations deleted)

{com}. 
. foreach y in benefits {c -(}
{txt}  2{com}. foreach x in extracted {c -(}
{txt}  3{com}. logit `y' `x' , cluster(bill)
{txt}  4{com}. outreg2 using regression_coll_comp_`y'_`x', tex append keep(`x') label title("Private Benefits - Collapsed - Bill Rejected") addtext(Robust SE, YES)
{txt}  5{com}. logit `y' `x' list_constnum career_minister_pre committee_chair_col, cluster(bill)
{txt}  6{com}. outreg2 using regression_coll_comp_`y'_`x', tex append keep(`x' list_constnum career_minister_pre committee_chair_col) label title("Private Benefits - Collapsed - Bill Rejected") addtext(Robust SE, YES)
{txt}  7{com}. logit `y' `x' list_constnum career_minister_pre committee_chair_col i.party2_st1, cluster(bill)
{txt}  8{com}. outreg2 using regression_coll_comp_`y'_`x', tex append keep( `x' list_constnum career_minister_pre committee_chair_col) label title("Private Benefits - Collapsed - Bill Rejected") addtext(Robust SE, YES, Party FE, YES)
{txt}  9{com}. {c )-}
{txt} 10{com}. {c )-}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -395.3362}  
Iteration 1:{space 3}log pseudolikelihood = {res:-395.31236}  
Iteration 2:{space 3}log pseudolikelihood = {res:-395.31235}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       663
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      0.06
{txt}{col 49}Prob > chi2{col 67}= {res}    0.8133
{txt}Log pseudolikelihood = {res}-395.31235{txt}{col 49}Pseudo R2{col 67}= {res}    0.0001

{txt}{ralign 78:(Std. Err. adjusted for {res:350} clusters in bill)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    benefits{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}extracted {c |}{col 14}{res}{space 2}-.1007731{col 26}{space 2} .4267269{col 37}{space 1}   -0.24{col 46}{space 3}0.813{col 54}{space 4}-.9371425{col 67}{space 3} .7355964
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.9207675{col 26}{space 2} .1044694{col 37}{space 1}   -8.81{col 46}{space 3}0.000{col 54}{space 4}-1.125524{col 67}{space 3}-.7160113
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_coll_comp_benefits_extracted.tex"'"':regression_coll_comp_benefits_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_coll_comp_benefits_extracted.txt", label"':seeout}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -395.3362}  
Iteration 1:{space 3}log pseudolikelihood = {res:-391.84609}  
Iteration 2:{space 3}log pseudolikelihood = {res: -391.8178}  
Iteration 3:{space 3}log pseudolikelihood = {res: -391.8178}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       663
{txt}{col 49}Wald chi2({res}4{txt}){col 67}= {res}      7.81
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0990
{txt}Log pseudolikelihood = {res} -391.8178{txt}{col 49}Pseudo R2{col 67}= {res}    0.0089

{txt}{ralign 85:(Std. Err. adjusted for {res:350} clusters in bill)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}           benefits{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}extracted {c |}{col 21}{res}{space 2}-.1086512{col 33}{space 2} .4389585{col 44}{space 1}   -0.25{col 53}{space 3}0.805{col 61}{space 4}-.9689941{col 74}{space 3} .7516917
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}  .076928{col 33}{space 2} .2207821{col 44}{space 1}    0.35{col 53}{space 3}0.728{col 61}{space 4}-.3557969{col 74}{space 3}  .509653
{txt}career_minister_pre {c |}{col 21}{res}{space 2} .1945286{col 33}{space 2} .2128248{col 44}{space 1}    0.91{col 53}{space 3}0.361{col 61}{space 4}-.2226003{col 74}{space 3} .6116575
{txt}committee_chair_col {c |}{col 21}{res}{space 2} .5675861{col 33}{space 2} .2643088{col 44}{space 1}    2.15{col 53}{space 3}0.032{col 61}{space 4} .0495503{col 74}{space 3} 1.085622
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.109575{col 33}{space 2}  .161759{col 44}{space 1}   -6.86{col 53}{space 3}0.000{col 61}{space 4}-1.426617{col 74}{space 3}-.7925333
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_coll_comp_benefits_extracted.tex"'"':regression_coll_comp_benefits_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_coll_comp_benefits_extracted.txt", label"':seeout}

note: 4.party2_st1 != 0 predicts success perfectly
      4.party2_st1 dropped and 1 obs not used

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-394.07395}  
Iteration 1:{space 3}log pseudolikelihood = {res: -381.9365}  
Iteration 2:{space 3}log pseudolikelihood = {res:-381.63298}  
Iteration 3:{space 3}log pseudolikelihood = {res:-381.63225}  
Iteration 4:{space 3}log pseudolikelihood = {res:-381.63225}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       662
{txt}{col 49}Wald chi2({res}9{txt}){col 67}= {res}     19.55
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0209
{txt}Log pseudolikelihood = {res}-381.63225{txt}{col 49}Pseudo R2{col 67}= {res}    0.0316

{txt}{ralign 85:(Std. Err. adjusted for {res:350} clusters in bill)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}           benefits{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}extracted {c |}{col 21}{res}{space 2}-.0952169{col 33}{space 2} .4301691{col 44}{space 1}   -0.22{col 53}{space 3}0.825{col 61}{space 4}-.9383329{col 74}{space 3} .7478991
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.2371583{col 33}{space 2} .2374981{col 44}{space 1}   -1.00{col 53}{space 3}0.318{col 61}{space 4}-.7026461{col 74}{space 3} .2283295
{txt}career_minister_pre {c |}{col 21}{res}{space 2}-.1015577{col 33}{space 2} .3409943{col 44}{space 1}   -0.30{col 53}{space 3}0.766{col 61}{space 4}-.7698943{col 74}{space 3} .5667789
{txt}committee_chair_col {c |}{col 21}{res}{space 2} .4315278{col 33}{space 2}  .293757{col 44}{space 1}    1.47{col 53}{space 3}0.142{col 61}{space 4}-.1442253{col 74}{space 3} 1.007281
{txt}{space 19} {c |}
{space 9}party2_st1 {c |}
{space 17}2  {c |}{col 21}{res}{space 2}-1.636582{col 33}{space 2} .5813996{col 44}{space 1}   -2.81{col 53}{space 3}0.005{col 61}{space 4}-2.776104{col 74}{space 3}-.4970596
{txt}{space 17}3  {c |}{col 21}{res}{space 2}-.4938832{col 33}{space 2} .5759819{col 44}{space 1}   -0.86{col 53}{space 3}0.391{col 61}{space 4}-1.622787{col 74}{space 3} .6350206
{txt}{space 7}3.285714388  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 17}5  {c |}{col 21}{res}{space 2}-.4668465{col 33}{space 2} .7065453{col 44}{space 1}   -0.66{col 53}{space 3}0.509{col 61}{space 4} -1.85165{col 74}{space 3}  .917957
{txt}{space 17}6  {c |}{col 21}{res}{space 2} -.446752{col 33}{space 2} .5601737{col 44}{space 1}   -0.80{col 53}{space 3}0.425{col 61}{space 4}-1.544672{col 74}{space 3} .6511683
{txt}{space 17}7  {c |}{col 21}{res}{space 2}-.7546349{col 33}{space 2}  .640176{col 44}{space 1}   -1.18{col 53}{space 3}0.238{col 61}{space 4}-2.009357{col 74}{space 3} .5000869
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-.1197169{col 33}{space 2} .5315968{col 44}{space 1}   -0.23{col 53}{space 3}0.822{col 61}{space 4}-1.161628{col 74}{space 3} .9221937
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_coll_comp_benefits_extracted.tex"'"':regression_coll_comp_benefits_extracted.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_coll_comp_benefits_extracted.txt", label"':seeout}

{com}. 
. 
. ***************************************************************************
. **Analysis Benefits - Generalized Model************************************
. ***************************************************************************
. use "datasetNZballot_Aug2017_prepared.dta", clear
{txt}(Written by R.              )

{com}. 
. *Appendix (Table A23 - first 3 columns)
. foreach y in benefits_ord {c -(}
{txt}  2{com}. foreach x in extracted {c -(}
{txt}  3{com}. 
. gologit2 `y' `x', autofit cluster(MP)
{txt}  4{com}. outreg2 using regression_gologit, tex replace keep( `x') label title("Full Sample") 
{txt}  5{com}. gologit2 `y' `x' list_constnum career_minister_pre committee_chair, autofit cluster(MP) 
{txt}  6{com}. outreg2 using regression_gologit, tex append keep( `x' list_constnum career_minister_pre committee_chair) label title("Full Sample") 
{txt}  7{com}. gologit2 `y' `x' list_constnum career_minister_pre committee_chair period_2 period_3 party2, autofit cluster(MP) 
{txt}  8{com}. outreg2 using regression_gologit, tex append keep( `x' list_constnum career_minister_pre committee_chair) label title("Full Sample") addtext(Party FE, YES, Legislative Period FE, YES)
{txt}  9{com}. {c )-}
{txt} 10{com}. {c )-}

{txt}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}
Testing parallel lines assumption using the {res}.05{txt} level of significance...

Step{col 7}1: {col 11}Constraints for parallel lines {res}imposed for extracted {txt}(P Value = {res}0.0928{txt})
Step{col 7}2: {col 11}{res}All explanatory variables {txt}meet the pl assumption

Wald test of parallel lines assumption for the final model:

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[0]extracted - [1]extracted = 0{p_end}

{txt}{col 12}chi2(  1) ={res}    2.82
{txt}{col 10}Prob > chi2 =  {res}  0.0928

{txt}An insignificant test statistic indicates that the final model
{res}does not violate {txt}the proportional odds/ parallel lines assumption

If you re-estimate this exact same model with {res}gologit2{txt}, instead 
of {res}autofit {txt}you can save time by using the parameter

{res}pl(extracted)

{txt}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}

Generalized Ordered Logit Estimates{col 49}Number of obs{col 67}= {res}     3,056
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      3.01
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0829
{txt}Log pseudolikelihood = {res}-1972.0043{txt}{col 49}Pseudo R2{col 67}= {res}    0.0007

{p 0 7}{space 1}{text:( 1)}{space 1} [0]extracted - [1]extracted = 0{p_end}
{txt}{ralign 78:(Std. Err. adjusted for {res:151} clusters in MP)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}benefits_ord{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}0            {txt}{c |}
{space 3}extracted {c |}{col 14}{res}{space 2} .3358955{col 26}{space 2} .1936873{col 37}{space 1}    1.73{col 46}{space 3}0.083{col 54}{space 4}-.0437247{col 67}{space 3} .7155157
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.9381194{col 26}{space 2} .1460409{col 37}{space 1}   -6.42{col 46}{space 3}0.000{col 54}{space 4}-1.224354{col 67}{space 3}-.6518845
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1            {txt}{c |}
{space 3}extracted {c |}{col 14}{res}{space 2} .3358955{col 26}{space 2} .1936873{col 37}{space 1}    1.73{col 46}{space 3}0.083{col 54}{space 4}-.0437247{col 67}{space 3} .7155157
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-4.444451{col 26}{space 2} .5033609{col 37}{space 1}   -8.83{col 46}{space 3}0.000{col 54}{space 4} -5.43102{col 67}{space 3}-3.457882
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_gologit.tex"'"':regression_gologit.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_gologit.txt", label"':seeout}

{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}
Testing parallel lines assumption using the {res}.05{txt} level of significance...

Step{col 7}1: {col 11}Constraints for parallel lines {res}imposed for committee_chair {txt}(P Value = {res}0.8530{txt})
Step{col 7}2: {col 11}Constraints for parallel lines {res}imposed for list_constnum {txt}(P Value = {res}0.4772{txt})
Step{col 7}3: {col 11}Constraints for parallel lines {res}imposed for career_minister_pre {txt}(P Value = {res}0.3677{txt})
Step{col 7}4: {col 11}Constraints for parallel lines {res}imposed for extracted {txt}(P Value = {res}0.1016{txt})
Step{col 7}5: {col 11}{res}All explanatory variables {txt}meet the pl assumption

Wald test of parallel lines assumption for the final model:

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[0]committee_chair - [1]committee_chair = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} [0]list_constnum - [1]list_constnum = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} [0]career_minister_pre - [1]career_minister_pre = 0{p_end}
{p 0 7}{space 1}{text:( 4)}{space 1} [0]extracted - [1]extracted = 0{p_end}

{txt}{col 12}chi2(  4) ={res}    6.44
{txt}{col 10}Prob > chi2 =  {res}  0.1684

{txt}An insignificant test statistic indicates that the final model
{res}does not violate {txt}the proportional odds/ parallel lines assumption

If you re-estimate this exact same model with {res}gologit2{txt}, instead 
of {res}autofit {txt}you can save time by using the parameter

{res}pl(committee_chair list_constnum career_minister_pre extracted)

{txt}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}

Generalized Ordered Logit Estimates{col 49}Number of obs{col 67}= {res}     3,056
{txt}{col 49}Wald chi2({res}4{txt}){col 67}= {res}     11.76
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0193
{txt}Log pseudolikelihood = {res}-1934.3928{txt}{col 49}Pseudo R2{col 67}= {res}    0.0198

{p 0 7}{space 1}{text:( 1)}{space 1} [0]committee_chair - [1]committee_chair = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} [0]list_constnum - [1]list_constnum = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} [0]career_minister_pre - [1]career_minister_pre = 0{p_end}
{p 0 7}{space 1}{text:( 4)}{space 1} [0]extracted - [1]extracted = 0{p_end}
{txt}{ralign 85:(Std. Err. adjusted for {res:151} clusters in MP)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}       benefits_ord{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}0                   {txt}{c |}
{space 10}extracted {c |}{col 21}{res}{space 2} .3258493{col 33}{space 2}  .192633{col 44}{space 1}    1.69{col 53}{space 3}0.091{col 61}{space 4}-.0517044{col 74}{space 3}  .703403
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.3458212{col 33}{space 2} .2934322{col 44}{space 1}   -1.18{col 53}{space 3}0.239{col 61}{space 4}-.9209377{col 74}{space 3} .2292953
{txt}career_minister_pre {c |}{col 21}{res}{space 2}  .474326{col 33}{space 2} .2886072{col 44}{space 1}    1.64{col 53}{space 3}0.100{col 61}{space 4}-.0913337{col 74}{space 3} 1.039986
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .7877044{col 33}{space 2} .3492099{col 44}{space 1}    2.26{col 53}{space 3}0.024{col 61}{space 4} .1032657{col 74}{space 3} 1.472143
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.082099{col 33}{space 2} .2389377{col 44}{space 1}   -4.53{col 53}{space 3}0.000{col 61}{space 4}-1.550409{col 74}{space 3}-.6137901
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1                   {txt}{c |}
{space 10}extracted {c |}{col 21}{res}{space 2} .3258493{col 33}{space 2}  .192633{col 44}{space 1}    1.69{col 53}{space 3}0.091{col 61}{space 4}-.0517044{col 74}{space 3}  .703403
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.3458212{col 33}{space 2} .2934322{col 44}{space 1}   -1.18{col 53}{space 3}0.239{col 61}{space 4}-.9209377{col 74}{space 3} .2292953
{txt}career_minister_pre {c |}{col 21}{res}{space 2}  .474326{col 33}{space 2} .2886072{col 44}{space 1}    1.64{col 53}{space 3}0.100{col 61}{space 4}-.0913337{col 74}{space 3} 1.039986
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .7877044{col 33}{space 2} .3492099{col 44}{space 1}    2.26{col 53}{space 3}0.024{col 61}{space 4} .1032657{col 74}{space 3} 1.472143
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-4.629244{col 33}{space 2} .5784625{col 44}{space 1}   -8.00{col 53}{space 3}0.000{col 61}{space 4}-5.763009{col 74}{space 3}-3.495478
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_gologit.tex"'"':regression_gologit.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_gologit.txt", label"':seeout}

{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}
Testing parallel lines assumption using the {res}.05{txt} level of significance...

Step{col 7}1: {col 11}Constraints for parallel lines {res}imposed for committee_chair {txt}(P Value = {res}0.8406{txt})
Step{col 7}2: {col 11}Constraints for parallel lines {res}imposed for list_constnum {txt}(P Value = {res}0.7865{txt})
Step{col 7}3: {col 11}Constraints for parallel lines {res}imposed for career_minister_pre {txt}(P Value = {res}0.3526{txt})
Step{col 7}4: {col 11}Constraints for parallel lines {res}imposed for party2 {txt}(P Value = {res}0.3199{txt})
Step{col 7}5: {col 11}Constraints for parallel lines {res}imposed for extracted {txt}(P Value = {res}0.0857{txt})
Step{col 7}6: {col 11}Constraints for parallel lines {res}are not imposed for 
{col 11}period_2 {txt}(P Value = {res}0.00000{txt})
{res}{col 11}period_3 {txt}(P Value = {res}0.00000{txt})

Wald test of parallel lines assumption for the final model:

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[0]committee_chair - [1]committee_chair = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} [0]list_constnum - [1]list_constnum = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} [0]career_minister_pre - [1]career_minister_pre = 0{p_end}
{p 0 7}{space 1}{text:( 4)}{space 1} [0]party2 - [1]party2 = 0{p_end}
{p 0 7}{space 1}{text:( 5)}{space 1} [0]extracted - [1]extracted = 0{p_end}

{txt}{col 12}chi2(  5) ={res}    6.84
{txt}{col 10}Prob > chi2 =  {res}  0.2325

{txt}An insignificant test statistic indicates that the final model
{res}does not violate {txt}the proportional odds/ parallel lines assumption

If you re-estimate this exact same model with {res}gologit2{txt}, instead 
of {res}autofit {txt}you can save time by using the parameter

{res}pl(committee_chair list_constnum career_minister_pre party2 extracted)

{txt}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}

Generalized Ordered Logit Estimates{col 49}Number of obs{col 67}= {res}     3,056
{txt}{col 49}Wald chi2({res}9{txt}){col 67}= {res}    921.03
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-1873.8715{txt}{col 49}Pseudo R2{col 67}= {res}    0.0504

{p 0 7}{space 1}{text:( 1)}{space 1} [0]committee_chair - [1]committee_chair = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} [0]list_constnum - [1]list_constnum = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} [0]career_minister_pre - [1]career_minister_pre = 0{p_end}
{p 0 7}{space 1}{text:( 4)}{space 1} [0]party2 - [1]party2 = 0{p_end}
{p 0 7}{space 1}{text:( 5)}{space 1} [0]extracted - [1]extracted = 0{p_end}
{txt}{ralign 85:(Std. Err. adjusted for {res:151} clusters in MP)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}       benefits_ord{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}0                   {txt}{c |}
{space 10}extracted {c |}{col 21}{res}{space 2} .3897452{col 33}{space 2} .1994672{col 44}{space 1}    1.95{col 53}{space 3}0.051{col 61}{space 4}-.0012033{col 74}{space 3} .7806937
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.3955134{col 33}{space 2} .2943581{col 44}{space 1}   -1.34{col 53}{space 3}0.179{col 61}{space 4}-.9724447{col 74}{space 3} .1814179
{txt}career_minister_pre {c |}{col 21}{res}{space 2} .7322225{col 33}{space 2} .3329739{col 44}{space 1}    2.20{col 53}{space 3}0.028{col 61}{space 4} .0796056{col 74}{space 3} 1.384839
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .4761593{col 33}{space 2} .3528649{col 44}{space 1}    1.35{col 53}{space 3}0.177{col 61}{space 4}-.2154431{col 74}{space 3} 1.167762
{txt}{space 11}period_2 {c |}{col 21}{res}{space 2} .8317963{col 33}{space 2} .4151111{col 44}{space 1}    2.00{col 53}{space 3}0.045{col 61}{space 4} .0181936{col 74}{space 3} 1.645399
{txt}{space 11}period_3 {c |}{col 21}{res}{space 2} .9688674{col 33}{space 2} .4435059{col 44}{space 1}    2.18{col 53}{space 3}0.029{col 61}{space 4} .0996119{col 74}{space 3} 1.838123
{txt}{space 13}party2 {c |}{col 21}{res}{space 2} .1616679{col 33}{space 2} .0899223{col 44}{space 1}    1.80{col 53}{space 3}0.072{col 61}{space 4}-.0145767{col 74}{space 3} .3379124
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-2.595836{col 33}{space 2}  .657881{col 44}{space 1}   -3.95{col 53}{space 3}0.000{col 61}{space 4} -3.88526{col 74}{space 3}-1.306413
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1                   {txt}{c |}
{space 10}extracted {c |}{col 21}{res}{space 2} .3897452{col 33}{space 2} .1994672{col 44}{space 1}    1.95{col 53}{space 3}0.051{col 61}{space 4}-.0012033{col 74}{space 3} .7806937
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.3955134{col 33}{space 2} .2943581{col 44}{space 1}   -1.34{col 53}{space 3}0.179{col 61}{space 4}-.9724447{col 74}{space 3} .1814179
{txt}career_minister_pre {c |}{col 21}{res}{space 2} .7322225{col 33}{space 2} .3329739{col 44}{space 1}    2.20{col 53}{space 3}0.028{col 61}{space 4} .0796056{col 74}{space 3} 1.384839
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .4761593{col 33}{space 2} .3528649{col 44}{space 1}    1.35{col 53}{space 3}0.177{col 61}{space 4}-.2154431{col 74}{space 3} 1.167762
{txt}{space 11}period_2 {c |}{col 21}{res}{space 2}  15.1092{col 33}{space 2} .6083422{col 44}{space 1}   24.84{col 53}{space 3}0.000{col 61}{space 4} 13.91687{col 74}{space 3} 16.30153
{txt}{space 11}period_3 {c |}{col 21}{res}{space 2} 13.66513{col 33}{space 2} .9857152{col 44}{space 1}   13.86{col 53}{space 3}0.000{col 61}{space 4} 11.73316{col 74}{space 3}  15.5971
{txt}{space 13}party2 {c |}{col 21}{res}{space 2} .1616679{col 33}{space 2} .0899223{col 44}{space 1}    1.80{col 53}{space 3}0.072{col 61}{space 4}-.0145767{col 74}{space 3} .3379124
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-19.79481{col 33}{space 2} .4880619{col 44}{space 1}  -40.56{col 53}{space 3}0.000{col 61}{space 4}-20.75139{col 74}{space 3}-18.83822
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"regression_gologit.tex"'"':regression_gologit.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_gologit.txt", label"':seeout}

{com}. 
. 
. drop if bill_passed == 0
{txt}(101 observations deleted)

{com}. 
. *Appendix (Table A23 - 4th column)
. 
. gologit2 benefits_ord extracted list_constnum career_minister_pre committee_chair period_2 period_3 party2, autofit cluster(MP)

{txt}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}
Testing parallel lines assumption using the {res}.05{txt} level of significance...

Step{col 7}1: {col 11}Constraints for parallel lines {res}imposed for committee_chair {txt}(P Value = {res}0.8415{txt})
Step{col 7}2: {col 11}Constraints for parallel lines {res}imposed for list_constnum {txt}(P Value = {res}0.7839{txt})
Step{col 7}3: {col 11}Constraints for parallel lines {res}imposed for party2 {txt}(P Value = {res}0.3800{txt})
Step{col 7}4: {col 11}Constraints for parallel lines {res}imposed for career_minister_pre {txt}(P Value = {res}0.3047{txt})
Step{col 7}5: {col 11}Constraints for parallel lines {res}imposed for extracted {txt}(P Value = {res}0.3735{txt})
Step{col 7}6: {col 11}Constraints for parallel lines {res}are not imposed for 
{col 11}period_2 {txt}(P Value = {res}0.00000{txt})
{res}{col 11}period_3 {txt}(P Value = {res}0.00000{txt})

Wald test of parallel lines assumption for the final model:

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[0]committee_chair - [1]committee_chair = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} [0]list_constnum - [1]list_constnum = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} [0]party2 - [1]party2 = 0{p_end}
{p 0 7}{space 1}{text:( 4)}{space 1} [0]career_minister_pre - [1]career_minister_pre = 0{p_end}
{p 0 7}{space 1}{text:( 5)}{space 1} [0]extracted - [1]extracted = 0{p_end}

{txt}{col 12}chi2(  5) ={res}    4.08
{txt}{col 10}Prob > chi2 =  {res}  0.5381

{txt}An insignificant test statistic indicates that the final model
{res}does not violate {txt}the proportional odds/ parallel lines assumption

If you re-estimate this exact same model with {res}gologit2{txt}, instead 
of {res}autofit {txt}you can save time by using the parameter

{res}pl(committee_chair list_constnum party2 career_minister_pre extracted)

{txt}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}

Generalized Ordered Logit Estimates{col 49}Number of obs{col 67}= {res}     2,955
{txt}{col 49}Wald chi2({res}9{txt}){col 67}= {res}    913.61
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-1804.8008{txt}{col 49}Pseudo R2{col 67}= {res}    0.0534

{p 0 7}{space 1}{text:( 1)}{space 1} [0]committee_chair - [1]committee_chair = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} [0]list_constnum - [1]list_constnum = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} [0]party2 - [1]party2 = 0{p_end}
{p 0 7}{space 1}{text:( 4)}{space 1} [0]career_minister_pre - [1]career_minister_pre = 0{p_end}
{p 0 7}{space 1}{text:( 5)}{space 1} [0]extracted - [1]extracted = 0{p_end}
{txt}{ralign 85:(Std. Err. adjusted for {res:151} clusters in MP)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}       benefits_ord{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}0                   {txt}{c |}
{space 10}extracted {c |}{col 21}{res}{space 2} 1.537872{col 33}{space 2} .5028208{col 44}{space 1}    3.06{col 53}{space 3}0.002{col 61}{space 4} .5523619{col 74}{space 3} 2.523383
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.4225948{col 33}{space 2}  .293929{col 44}{space 1}   -1.44{col 53}{space 3}0.151{col 61}{space 4}-.9986851{col 74}{space 3} .1534955
{txt}career_minister_pre {c |}{col 21}{res}{space 2} .7668813{col 33}{space 2} .3302636{col 44}{space 1}    2.32{col 53}{space 3}0.020{col 61}{space 4} .1195765{col 74}{space 3} 1.414186
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .4596277{col 33}{space 2} .3539604{col 44}{space 1}    1.30{col 53}{space 3}0.194{col 61}{space 4}-.2341219{col 74}{space 3} 1.153377
{txt}{space 11}period_2 {c |}{col 21}{res}{space 2} .8861529{col 33}{space 2} .4270012{col 44}{space 1}    2.08{col 53}{space 3}0.038{col 61}{space 4}  .049246{col 74}{space 3}  1.72306
{txt}{space 11}period_3 {c |}{col 21}{res}{space 2} 1.009827{col 33}{space 2} .4527655{col 44}{space 1}    2.23{col 53}{space 3}0.026{col 61}{space 4} .1224226{col 74}{space 3} 1.897231
{txt}{space 13}party2 {c |}{col 21}{res}{space 2} .1634191{col 33}{space 2} .0892698{col 44}{space 1}    1.83{col 53}{space 3}0.067{col 61}{space 4}-.0115465{col 74}{space 3} .3383847
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-2.646401{col 33}{space 2} .6582599{col 44}{space 1}   -4.02{col 53}{space 3}0.000{col 61}{space 4}-3.936567{col 74}{space 3}-1.356235
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1                   {txt}{c |}
{space 10}extracted {c |}{col 21}{res}{space 2} 1.537872{col 33}{space 2} .5028208{col 44}{space 1}    3.06{col 53}{space 3}0.002{col 61}{space 4} .5523619{col 74}{space 3} 2.523383
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.4225948{col 33}{space 2}  .293929{col 44}{space 1}   -1.44{col 53}{space 3}0.151{col 61}{space 4}-.9986851{col 74}{space 3} .1534955
{txt}career_minister_pre {c |}{col 21}{res}{space 2} .7668813{col 33}{space 2} .3302636{col 44}{space 1}    2.32{col 53}{space 3}0.020{col 61}{space 4} .1195765{col 74}{space 3} 1.414186
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .4596277{col 33}{space 2} .3539604{col 44}{space 1}    1.30{col 53}{space 3}0.194{col 61}{space 4}-.2341219{col 74}{space 3} 1.153377
{txt}{space 11}period_2 {c |}{col 21}{res}{space 2} 14.35009{col 33}{space 2} .5915741{col 44}{space 1}   24.26{col 53}{space 3}0.000{col 61}{space 4} 13.19062{col 74}{space 3} 15.50955
{txt}{space 11}period_3 {c |}{col 21}{res}{space 2} 12.98503{col 33}{space 2} .9746351{col 44}{space 1}   13.32{col 53}{space 3}0.000{col 61}{space 4} 11.07478{col 74}{space 3} 14.89528
{txt}{space 13}party2 {c |}{col 21}{res}{space 2} .1634191{col 33}{space 2} .0892698{col 44}{space 1}    1.83{col 53}{space 3}0.067{col 61}{space 4}-.0115465{col 74}{space 3} .3383847
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-19.07719{col 33}{space 2} .4615815{col 44}{space 1}  -41.33{col 53}{space 3}0.000{col 61}{space 4}-19.98187{col 74}{space 3}-18.17251
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using regression_gologit, tex append keep( extracted list_constnum career_minister_pre committee_chair) label title("Successful") addtext(Party FE, YES, Legislative Period FE, YES)
{txt}{stata `"shellout using `"regression_gologit.tex"'"':regression_gologit.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_gologit.txt", label"':seeout}

{com}. 
. 
. *Appendix (Table A23 - 5th column)
. use datasetNZballot_Aug2017_prepared, clear
{txt}(Written by R.              )

{com}. 
. drop if bill_passed == 1
{txt}(16 observations deleted)

{com}. 
. gologit2 benefits_ord extracted list_constnum career_minister_pre committee_chair period_2 period_3 party2, autofit cluster(MP)

{txt}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}
Testing parallel lines assumption using the {res}.05{txt} level of significance...

Step{col 7}1: {col 11}Constraints for parallel lines {res}imposed for extracted {txt}(P Value = {res}0.8040{txt})
Step{col 7}2: {col 11}Constraints for parallel lines {res}imposed for committee_chair {txt}(P Value = {res}0.7580{txt})
Step{col 7}3: {col 11}Constraints for parallel lines {res}imposed for list_constnum {txt}(P Value = {res}0.7604{txt})
Step{col 7}4: {col 11}Constraints for parallel lines {res}imposed for career_minister_pre {txt}(P Value = {res}0.3768{txt})
Step{col 7}5: {col 11}Constraints for parallel lines {res}imposed for party2 {txt}(P Value = {res}0.2823{txt})
Step{col 7}6: {col 11}Constraints for parallel lines {res}are not imposed for 
{col 11}period_2 {txt}(P Value = {res}0.00000{txt})
{res}{col 11}period_3 {txt}(P Value = {res}0.00000{txt})

Wald test of parallel lines assumption for the final model:

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[0]extracted - [1]extracted = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} [0]committee_chair - [1]committee_chair = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} [0]list_constnum - [1]list_constnum = 0{p_end}
{p 0 7}{space 1}{text:( 4)}{space 1} [0]career_minister_pre - [1]career_minister_pre = 0{p_end}
{p 0 7}{space 1}{text:( 5)}{space 1} [0]party2 - [1]party2 = 0{p_end}

{txt}{col 12}chi2(  5) ={res}    3.15
{txt}{col 10}Prob > chi2 =  {res}  0.6774

{txt}An insignificant test statistic indicates that the final model
{res}does not violate {txt}the proportional odds/ parallel lines assumption

If you re-estimate this exact same model with {res}gologit2{txt}, instead 
of {res}autofit {txt}you can save time by using the parameter

{res}pl(extracted committee_chair list_constnum career_minister_pre party2)

{txt}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}{c -}

Generalized Ordered Logit Estimates{col 49}Number of obs{col 67}= {res}     3,040
{txt}{col 49}Wald chi2({res}9{txt}){col 67}= {res}   1082.90
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-1857.8606{txt}{col 49}Pseudo R2{col 67}= {res}    0.0482

{p 0 7}{space 1}{text:( 1)}{space 1} [0]extracted - [1]extracted = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} [0]committee_chair - [1]committee_chair = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} [0]list_constnum - [1]list_constnum = 0{p_end}
{p 0 7}{space 1}{text:( 4)}{space 1} [0]career_minister_pre - [1]career_minister_pre = 0{p_end}
{p 0 7}{space 1}{text:( 5)}{space 1} [0]party2 - [1]party2 = 0{p_end}
{txt}{ralign 85:(Std. Err. adjusted for {res:151} clusters in MP)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}       benefits_ord{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}0                   {txt}{c |}
{space 10}extracted {c |}{col 21}{res}{space 2} .1905737{col 33}{space 2} .2167946{col 44}{space 1}    0.88{col 53}{space 3}0.379{col 61}{space 4}-.2343359{col 74}{space 3} .6154833
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.3936766{col 33}{space 2} .2955917{col 44}{space 1}   -1.33{col 53}{space 3}0.183{col 61}{space 4}-.9730257{col 74}{space 3} .1856726
{txt}career_minister_pre {c |}{col 21}{res}{space 2} .7255823{col 33}{space 2}  .334762{col 44}{space 1}    2.17{col 53}{space 3}0.030{col 61}{space 4} .0694609{col 74}{space 3} 1.381704
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .4764842{col 33}{space 2} .3563529{col 44}{space 1}    1.34{col 53}{space 3}0.181{col 61}{space 4}-.2219547{col 74}{space 3} 1.174923
{txt}{space 11}period_2 {c |}{col 21}{res}{space 2} .8156035{col 33}{space 2} .4191621{col 44}{space 1}    1.95{col 53}{space 3}0.052{col 61}{space 4}-.0059391{col 74}{space 3} 1.637146
{txt}{space 11}period_3 {c |}{col 21}{res}{space 2} .9556985{col 33}{space 2} .4477952{col 44}{space 1}    2.13{col 53}{space 3}0.033{col 61}{space 4}  .078036{col 74}{space 3} 1.833361
{txt}{space 13}party2 {c |}{col 21}{res}{space 2} .1589657{col 33}{space 2} .0902332{col 44}{space 1}    1.76{col 53}{space 3}0.078{col 61}{space 4}-.0178882{col 74}{space 3} .3358197
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-2.568465{col 33}{space 2} .6607858{col 44}{space 1}   -3.89{col 53}{space 3}0.000{col 61}{space 4}-3.863581{col 74}{space 3}-1.273349
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1                   {txt}{c |}
{space 10}extracted {c |}{col 21}{res}{space 2} .1905737{col 33}{space 2} .2167946{col 44}{space 1}    0.88{col 53}{space 3}0.379{col 61}{space 4}-.2343359{col 74}{space 3} .6154833
{txt}{space 6}list_constnum {c |}{col 21}{res}{space 2}-.3936766{col 33}{space 2} .2955917{col 44}{space 1}   -1.33{col 53}{space 3}0.183{col 61}{space 4}-.9730257{col 74}{space 3} .1856726
{txt}career_minister_pre {c |}{col 21}{res}{space 2} .7255823{col 33}{space 2}  .334762{col 44}{space 1}    2.17{col 53}{space 3}0.030{col 61}{space 4} .0694609{col 74}{space 3} 1.381704
{txt}{space 4}committee_chair {c |}{col 21}{res}{space 2} .4764842{col 33}{space 2} .3563529{col 44}{space 1}    1.34{col 53}{space 3}0.181{col 61}{space 4}-.2219547{col 74}{space 3} 1.174923
{txt}{space 11}period_2 {c |}{col 21}{res}{space 2} 14.84067{col 33}{space 2} .5713069{col 44}{space 1}   25.98{col 53}{space 3}0.000{col 61}{space 4} 13.72093{col 74}{space 3} 15.96041
{txt}{space 11}period_3 {c |}{col 21}{res}{space 2}  13.4601{col 33}{space 2} .9657251{col 44}{space 1}   13.94{col 53}{space 3}0.000{col 61}{space 4} 11.56732{col 74}{space 3} 15.35289
{txt}{space 13}party2 {c |}{col 21}{res}{space 2} .1589657{col 33}{space 2} .0902332{col 44}{space 1}    1.76{col 53}{space 3}0.078{col 61}{space 4}-.0178882{col 74}{space 3} .3358197
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-19.56373{col 33}{space 2} .4256967{col 44}{space 1}  -45.96{col 53}{space 3}0.000{col 61}{space 4}-20.39808{col 74}{space 3}-18.72938
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. outreg2 using regression_gologit, tex append keep( extracted list_constnum career_minister_pre committee_chair) label title("Unsuccessful") addtext(Party FE, YES, Legislative Period FE, YES)
{txt}{stata `"shellout using `"regression_gologit.tex"'"':regression_gologit.tex}
{browse `"C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code"' :dir}{com} : {txt}{stata `"seeout using "regression_gologit.txt", label"':seeout}

{com}. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\matia\Dropbox\cartelle condivise\NZ-Careers\data and analysis\-analysis\Replication_Code\appendix_log.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}16 Nov 2018, 15:27:55
{txt}{.-}
{smcl}
{txt}{sf}{ul off}